# A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism

Authors: Shoujun Tang; Mohammad Shabaz
Journal: Journal of Healthcare Engineering
 (2021)

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## Abstract

Face recognition is one of the popular areas of research in the field of computer vision. It is mainly used for identification and security system. One of the major challenges in face recognition is identification under numerous illumination environments by changing the direction of light or modifying the lighting magnitude. Exacting illumination invariant features is an effective approach to solve this problem. Conventional face recognition algorithms based on nonsubsampled contourlet transform (NSCT) and bionic mode are not capable enough to recognize the similar faces with great accuracy. Hence, in this paper, an attempt is made to propose an enhanced cerebellum-basal ganglia mechanism (CBGM) for face recognition. The integral projection and geometric feature assortment method are used to acquire the facial image features. The cognition model is deployed which is based on the cerebellum-basal ganglia mechanism and is applied for extraction of features from the face image to achieve greater accuracy for recognition of face images. The experimental results reveal that the enhanced CBGM algorithm can effectively recognize face images with greater accuracy. The recognition rate of 100 AR face images has been found to be 96.9%. The high recognition accuracy rate has been achieved by the proposed CBGM technique.


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## Body

## 1. Introduction

Face image recognition is an important title in the field of pattern recognition research, and it is also a very active research direction. Face recognition (FR) techniques make use of computers to categorize faces from static, still, or video images. The problem can normally be labeled as giving a still or video image [1], using the stored face database to confirm whether the person in the scene is a specific person face confirmation in the library or which person in the library face recognition [2]. FR is usually categorized into three parts: the face is detected and segmented from a scene with a complex background and then face features are extracted. Although it is quite easy for humans to recognize faces from complex backgrounds, automatic recognition of faces is a very difficult process for computers [3]. The difficulty of the problem is manifested by three aspects: first, each face has eyes, the nose, the mouth distributed in a certain spatial structure. While the difference between people is significant, it will result in certain features that are not fixed [4], such as glasses and beards, making it difficult to detect and recognize the face. Finally, because the face is a nonrigid three-dimensional object, the difference in posture and light also makes the image always changeable. In short, face detection and recognition problems become very problematic. At the same time, FR research has become more challenging in areas such as image processing, pattern recognition, computer vision, artificial neural networks, and neurophysiology, and psychology [5].In the mining of facial recognition, many techniques in both spatial and frequency domains have been employed. Pictures are frequently taken in unrestricted conditions. It must therefore be preprocessed before extracting features. Preprocessing shall be performed to reduce the effect of noise, the variance of illumination, color intensity, backdrop, and orientation. The image recognition also depends upon the image quality, lighting condition, etc. A method to extract multiscale geometric features from geometrical considerations from a data cloud is proposed and analyzed [6]. Feature extraction and dimensionality reduction using pattern analysis were proposed for face recognition [7]. An efficient FR model-based contourlet transform (CNT) and SVM were presented for feature reduction. A dimensional reduction of space per function was proposed according to the entropy of the conversion coefficients. Selected features were used to recognize the face images using a support vector machine (SVM) classifier [8].Face recognition is arduous under illumination variations, in particular for a single image-based method. The successful technique is to extract invariant illumination characteristics to identify the images [9]. The experimental analysis demonstrates improved performance using automotive scene imagery, preprocessed, and invariant illumination. The ant colony optimization algorithm enables efficient collection by reducing the size of the characteristics of essential geometric structures in facial images [10].In many cases, such as bank warehouses, immigration management, tourist attractions, libraries, archives, and other services, entrance management is required to prevent illegal entry [11–15]. Therefore, identity verification has attracted more and more attention. At present, the manual identification method is widely used. The disadvantages are low recognition efficiency, heavy workload, the need for third-party support, and the inability to obtain remote recognition [16]. The facial image recognition technology based on the cerebellar-basal ganglion mechanism can effectively overcome the above shortcomings and realize automatic, efficient, and networked recognition. Today, many practical methods exist, such as fingerprint, retina, iris, genes, and other human biological characteristics. However, to ensure the right recognition rate, factors such as usability of identification, frankness, and psychological barriers to the subject being audited must also be taken into account [17].The contributions of the proposed framework can be summarized as follows: (1) study of the facial image recognition algorithm using CBGM, (2) experimental setup of facial recognition with enhanced CBGM algorithm, and (3) the efficiency of the proposed work which shall be demonstrated using comprehensive experimental results. It is compared with the conventional algorithms like face recognition algorithm based on NSCT and bionic pattern and weighted modular face recognition algorithm based onK-means clustering method.The rest of this work is systematized as follows: research on a facial image recognition algorithm based on the mechanism of cerebellar and basal ganglia is briefly discussed in Section2.1. The enhanced CBGM algorithm is illustrated in Section 2.2. The experimental results and analysis are exposed in Section 3. Discussion is provided in Section 4. The conclusions are presented in Section 5.


## 2. Research on Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism

### 2.1. Feature Extraction of the Face Image

Humans can recognize faces from a distance, even if facial details (such as eyes, nose, and mouth) are not visible. This suggests that all the geometric characteristics of the face are sufficient for identification [18]. Common facial features are local features of the eyes, nose, mouth, etc. Prior knowledge of the structure of the face is often used to pull out features [19]. The selected geometric elements must comply with the following requirements [20]: (1) the estimation of features is as simple as possible; (2) the dependence of the feature on the illumination is as low as possible; (3) the feature is not too sensitive to changes in facial expression; and (4) there should be sufficient information about the feature to identify the face.


#### 2.1.1. Integral Projection

The integral projection method is a very useful method to detect features of the face. This technique has been successfully applied to many complex facial recognitions.The integral projection method is not new. But this approach has some attractive advantages, such as low computational complexity and almost satisfactory accuracy [21]. Furthermore, the use of a plurality of smaller and somewhat wide feature windows does not require very precise detection of the position of the eyes and mouth. Therefore, this simple and low calculation method of integral projection is more suitable. The advantage of the integral projection method is the simplicity of the algorithm and the high speed of the calculation. The main problem of this method is its low accuracy. For example, eyebrows frequently interfere with proper pupil position detection, and glasses may also affect accurate eye position detection [22]. Figure 1 is an integral projection view of a facial image and Figure 2 is a pupil positioning map.Figure 1
Integral projection of face image.Figure 2
Face pupil localization map.


#### 2.1.2. Geometric Feature Selection

Based on the integral projection, the geometric feature extraction method is used to accurately locate the facial features. The selection of features should ensure the most representative, important, and minimally redundant information [23], and it is necessary to maintain a certain invariance and adaptability under certain external disturbances. Based on this requirement, a plurality of feature points of the face is located.When the shape and position features are used for recognition, the feature point is composed of a facial feature vector suitable for computer recognition [24]. Since the position of the point is greatly changed by the size and position of the image. The features are eye width, outer eye width, nose width, the mouth wide, the cheek width of the mouth, cheek width at the tip of the nose, lip height, distance between lower lip and chin, the distance between the upper lip and nose, and the height of left eye and right eye. The ratio of these features to the pupil distance is defined as a standardized feature.Once the eigenvectors are normalized, the eigenvalues remain essentially the same face rotation and image size [25]. This increases the accuracy and flexibility of identification [26].


### 2.2. Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism

#### 2.2.1. Behavioral Cognitive Model Based on Cerebellum-Basal Ganglia Mechanism

A behavioral cognitive computing model is a combination of the cognitive learning mechanism of the basal ganglia with the cerebellum monitoring mechanism. This model is based on the organization of the cerebellum and basal ganglia are constructed and used for face images [27]. Facial recognition allows for accurate facial recognition [28].


#### 2.2.2. Basic Principles of Operational Conditioning Related to the Basal Ganglia

Operating conditional reflexes emphasize the effect of operational outcomes on behavior [29]. The difference between operational learning of conditional reflection and supervised learning is that feedback information obtained from the environment is a signal of assessment and not a signal of error. There are three basic elements of learning control based on the principle of operational conditional reflection: behavior selection mechanism (selection behavior according to probability), evaluation mechanism, and orientation mechanism.


#### 2.2.3. Structure of the Behavioral Cognitive Computing Model for Coordination of the Cerebellum and Basal Ganglia

According to the working mode of the cerebellum and basal ganglia, a CB-BC-Based Cognition Computational Module [30] is proposed to simulate the cerebellum and basal ganglia mechanism. The model uses the operational reflection condition as its main learning mechanism and adopts the supervised behavior-learning structure [31]. As shown in Figure 3, the ultimate goal of learning is to transfer the agent from the initial state s0 to the target state sd.Figure 3
Behavioral cognition model structure based on cerebellum-basal ganglia mechanism.In Figure3, BG denotes basal ganglia; CB denotes cerebellum; IO denotes inferior olive; SN denotes substantia nigra; and CF denotes climbing fiber. The behavioral network part is realized by the joint action of BG and CB [32]. The evaluation part is attributed to BG, the solid line is the data stream, the dotted line is the learning algorithm [33], and the coordination factor ω is used to calculate the composite behavior, by using the form of weighted sum shown in(1)a←ωaE+1−ωas.In (1), aE is the exploratory behavior, which is implemented by the behavioral network aA=πAs,θ⟶ combined with probabilistic behavior selection, and as is the supervised behavior, which is a form of traditional feedback controller [34]. Supervised behavior provides intentions and solutions for the behavioral network in the early stage of learning, avoiding the excessive blind search. In the learning process, the coordination factor takes the form of an exponential increase; that is, the supervisor is not involved in the entire closed-loop control process.


#### 2.2.4. Face Image Recognition

The process of face image recognition using the behavioral cognitive computing model coordinated by the cerebellum and the basal ganglia is a learning process. The process continuously collects state information. The state information is the face image feature acquired in Section2.1. The current state is st and performs a behavior at; then, it is transferred to the next state st+1, to obtain the instant reward rt+1 and updates the behavior and evaluation network [35–39]. As time goes on, the learning system can continuously collect state and behavior information. Consequently, the training samples are obtained during the learning process, which is the facial image characteristic to be recognized. The specific implementation principle is as follows: the learning sample of the behavioral network learns according to the evaluation information. When the exploration behavior is performed and the system state is developed in a good direction, the behavioral network aA=πAs,θ⟶ tends to explore behavior. At the moment, the present state and the exploratory behavior become the learning samples of the network. This ensures that the network converges under the condition of multiple learning cycles, and the agent can find the proper behavior for himself through training on operating conditions. The evaluation network obtains sampling data by continuously collecting information on rewards and state transition and updates the evaluation network through time difference (ST) learning. The role of the initial supervisor is to reduce the search space and provide the sampling network information necessary for the learning process in the assessment network. This allows the learning system to ensure minimum standard performance and avoid adverse situations [40–42]. The following is a detailed behavioral cognitive computing model that is coordinated with the cerebellum and basal ganglia to implement the face image recognition process:(1) Basal Ganglia Evaluation Network. The evaluation value function Vs approximates the future reward discount and the estimate is shown in(2)Vst=Ert+1+γVst+1.Equation (2) is used to evaluate the behavior and is approximated by the network fs,w⟶. The evaluation information rt+1 and the evaluation value Vst+1 of the next-time state are used to estimate the evaluation information of the current state, and the TD error is used, as shown in(3)δt=rt+1+γVst+1−Vst.The evaluation network weight is updated andzt is the qualification trace for evaluating the network weight, by using the linear differential equation as in(4)zt=γλzt−1+∂Vst∂w⟶t.(2) Cerebellum-Basal Ganglia Behavioral Network. Behavioral strategy πAs is a state-to-behavior mapping that can be approximated by a network with parameters θ⟶, as shown in(5)aA=πAs,θ⟶.The selection of behavioraE obeys the Boltzmann-Gibbs probability distribution of(6)φa=1Zexp−εaEKBT.In (6), εaE=aE−aA2, when T is greater than 0, it is the thermodynamic temperature, where the degree of exploration of the behavior is characterized, the higher the temperature is, the greater the degree of exploration is and vice versa [43, 44]. KB=1.38×10−23Js/K is the Boltzmann constant, exp−εaE/KBT is the Boltzmann factor, Z is the distribution function, and there is(7)Z=∫−∞+∞exp−εaEKBTdaE=KBTπ.The update of parameterθ⟶ consists of two parts: one is to realize Δθ⟶CB by cerebellum CB and the other is to realize Δθ⟶BG by basal ganglia BG, as shown in(8)θ⟶←θ⟶+ωΔθ⟶BG+1−ωΔθ⟶CB.The supervisory error is expressed by(9)Es=12πss−πAs2.The weight change of the CB part of the behavioral network cerebellum is shown in(10)Δθ⟶CB=−α∂∇Es=α∂as−aA∇θπAs.The weight update of the BG part of the behavioral network basal ganglia uses an approximate strategy gradient estimation algorithm shown in(11)Δθ⟶BG=αδaE−aA∇θπAs,where a>0 is the step parameter of learning behavior network learning, 0≤ω≤1 is used to weigh two kinds of gradient information in the learning process, one is related to the second evaluation signal (also called internal reward δ), another kind of gradient information is related to the supervision behavior error as−aA. When the internal evaluation is good δ>0, cognitive behavior is updated to the direction of exploration behavior and supervision behavior [45–47] and the exploration behavior becomes a sample of network learning; on the contrary, the update direction is opposite and behavioral network search is more suitable for environmental behavior. The update formula of behavioral network based on cerebellum-basal ganglia mechanism is shown in(12)θ⟶=θ⟶+αδωaE−as+as−aA∇θπAs.The update algorithm of supervised behavior network weight shows that the TD error adjusts the supervisory error information; that is, the evaluation type learning priority is higher than the supervised learning. In this case, the supervisor is only an exploration source for exploring learning. In special cases, whenω=1, the exploration behavior plays a major role, and finally, the effective recognition of the face image is realized.


## 2.1. Feature Extraction of the Face Image

Humans can recognize faces from a distance, even if facial details (such as eyes, nose, and mouth) are not visible. This suggests that all the geometric characteristics of the face are sufficient for identification [18]. Common facial features are local features of the eyes, nose, mouth, etc. Prior knowledge of the structure of the face is often used to pull out features [19]. The selected geometric elements must comply with the following requirements [20]: (1) the estimation of features is as simple as possible; (2) the dependence of the feature on the illumination is as low as possible; (3) the feature is not too sensitive to changes in facial expression; and (4) there should be sufficient information about the feature to identify the face.


### 2.1.1. Integral Projection

The integral projection method is a very useful method to detect features of the face. This technique has been successfully applied to many complex facial recognitions.The integral projection method is not new. But this approach has some attractive advantages, such as low computational complexity and almost satisfactory accuracy [21]. Furthermore, the use of a plurality of smaller and somewhat wide feature windows does not require very precise detection of the position of the eyes and mouth. Therefore, this simple and low calculation method of integral projection is more suitable. The advantage of the integral projection method is the simplicity of the algorithm and the high speed of the calculation. The main problem of this method is its low accuracy. For example, eyebrows frequently interfere with proper pupil position detection, and glasses may also affect accurate eye position detection [22]. Figure 1 is an integral projection view of a facial image and Figure 2 is a pupil positioning map.Figure 1
Integral projection of face image.Figure 2
Face pupil localization map.


### 2.1.2. Geometric Feature Selection

Based on the integral projection, the geometric feature extraction method is used to accurately locate the facial features. The selection of features should ensure the most representative, important, and minimally redundant information [23], and it is necessary to maintain a certain invariance and adaptability under certain external disturbances. Based on this requirement, a plurality of feature points of the face is located.When the shape and position features are used for recognition, the feature point is composed of a facial feature vector suitable for computer recognition [24]. Since the position of the point is greatly changed by the size and position of the image. The features are eye width, outer eye width, nose width, the mouth wide, the cheek width of the mouth, cheek width at the tip of the nose, lip height, distance between lower lip and chin, the distance between the upper lip and nose, and the height of left eye and right eye. The ratio of these features to the pupil distance is defined as a standardized feature.Once the eigenvectors are normalized, the eigenvalues remain essentially the same face rotation and image size [25]. This increases the accuracy and flexibility of identification [26].


## 2.1.1. Integral Projection

The integral projection method is a very useful method to detect features of the face. This technique has been successfully applied to many complex facial recognitions.The integral projection method is not new. But this approach has some attractive advantages, such as low computational complexity and almost satisfactory accuracy [21]. Furthermore, the use of a plurality of smaller and somewhat wide feature windows does not require very precise detection of the position of the eyes and mouth. Therefore, this simple and low calculation method of integral projection is more suitable. The advantage of the integral projection method is the simplicity of the algorithm and the high speed of the calculation. The main problem of this method is its low accuracy. For example, eyebrows frequently interfere with proper pupil position detection, and glasses may also affect accurate eye position detection [22]. Figure 1 is an integral projection view of a facial image and Figure 2 is a pupil positioning map.Figure 1
Integral projection of face image.Figure 2
Face pupil localization map.


## 2.1.2. Geometric Feature Selection

Based on the integral projection, the geometric feature extraction method is used to accurately locate the facial features. The selection of features should ensure the most representative, important, and minimally redundant information [23], and it is necessary to maintain a certain invariance and adaptability under certain external disturbances. Based on this requirement, a plurality of feature points of the face is located.When the shape and position features are used for recognition, the feature point is composed of a facial feature vector suitable for computer recognition [24]. Since the position of the point is greatly changed by the size and position of the image. The features are eye width, outer eye width, nose width, the mouth wide, the cheek width of the mouth, cheek width at the tip of the nose, lip height, distance between lower lip and chin, the distance between the upper lip and nose, and the height of left eye and right eye. The ratio of these features to the pupil distance is defined as a standardized feature.Once the eigenvectors are normalized, the eigenvalues remain essentially the same face rotation and image size [25]. This increases the accuracy and flexibility of identification [26].


## 2.2. Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism

### 2.2.1. Behavioral Cognitive Model Based on Cerebellum-Basal Ganglia Mechanism

A behavioral cognitive computing model is a combination of the cognitive learning mechanism of the basal ganglia with the cerebellum monitoring mechanism. This model is based on the organization of the cerebellum and basal ganglia are constructed and used for face images [27]. Facial recognition allows for accurate facial recognition [28].


### 2.2.2. Basic Principles of Operational Conditioning Related to the Basal Ganglia

Operating conditional reflexes emphasize the effect of operational outcomes on behavior [29]. The difference between operational learning of conditional reflection and supervised learning is that feedback information obtained from the environment is a signal of assessment and not a signal of error. There are three basic elements of learning control based on the principle of operational conditional reflection: behavior selection mechanism (selection behavior according to probability), evaluation mechanism, and orientation mechanism.


### 2.2.3. Structure of the Behavioral Cognitive Computing Model for Coordination of the Cerebellum and Basal Ganglia

According to the working mode of the cerebellum and basal ganglia, a CB-BC-Based Cognition Computational Module [30] is proposed to simulate the cerebellum and basal ganglia mechanism. The model uses the operational reflection condition as its main learning mechanism and adopts the supervised behavior-learning structure [31]. As shown in Figure 3, the ultimate goal of learning is to transfer the agent from the initial state s0 to the target state sd.Figure 3
Behavioral cognition model structure based on cerebellum-basal ganglia mechanism.In Figure3, BG denotes basal ganglia; CB denotes cerebellum; IO denotes inferior olive; SN denotes substantia nigra; and CF denotes climbing fiber. The behavioral network part is realized by the joint action of BG and CB [32]. The evaluation part is attributed to BG, the solid line is the data stream, the dotted line is the learning algorithm [33], and the coordination factor ω is used to calculate the composite behavior, by using the form of weighted sum shown in(1)a←ωaE+1−ωas.In (1), aE is the exploratory behavior, which is implemented by the behavioral network aA=πAs,θ⟶ combined with probabilistic behavior selection, and as is the supervised behavior, which is a form of traditional feedback controller [34]. Supervised behavior provides intentions and solutions for the behavioral network in the early stage of learning, avoiding the excessive blind search. In the learning process, the coordination factor takes the form of an exponential increase; that is, the supervisor is not involved in the entire closed-loop control process.


### 2.2.4. Face Image Recognition

The process of face image recognition using the behavioral cognitive computing model coordinated by the cerebellum and the basal ganglia is a learning process. The process continuously collects state information. The state information is the face image feature acquired in Section2.1. The current state is st and performs a behavior at; then, it is transferred to the next state st+1, to obtain the instant reward rt+1 and updates the behavior and evaluation network [35–39]. As time goes on, the learning system can continuously collect state and behavior information. Consequently, the training samples are obtained during the learning process, which is the facial image characteristic to be recognized. The specific implementation principle is as follows: the learning sample of the behavioral network learns according to the evaluation information. When the exploration behavior is performed and the system state is developed in a good direction, the behavioral network aA=πAs,θ⟶ tends to explore behavior. At the moment, the present state and the exploratory behavior become the learning samples of the network. This ensures that the network converges under the condition of multiple learning cycles, and the agent can find the proper behavior for himself through training on operating conditions. The evaluation network obtains sampling data by continuously collecting information on rewards and state transition and updates the evaluation network through time difference (ST) learning. The role of the initial supervisor is to reduce the search space and provide the sampling network information necessary for the learning process in the assessment network. This allows the learning system to ensure minimum standard performance and avoid adverse situations [40–42]. The following is a detailed behavioral cognitive computing model that is coordinated with the cerebellum and basal ganglia to implement the face image recognition process:(1) Basal Ganglia Evaluation Network. The evaluation value function Vs approximates the future reward discount and the estimate is shown in(2)Vst=Ert+1+γVst+1.Equation (2) is used to evaluate the behavior and is approximated by the network fs,w⟶. The evaluation information rt+1 and the evaluation value Vst+1 of the next-time state are used to estimate the evaluation information of the current state, and the TD error is used, as shown in(3)δt=rt+1+γVst+1−Vst.The evaluation network weight is updated andzt is the qualification trace for evaluating the network weight, by using the linear differential equation as in(4)zt=γλzt−1+∂Vst∂w⟶t.(2) Cerebellum-Basal Ganglia Behavioral Network. Behavioral strategy πAs is a state-to-behavior mapping that can be approximated by a network with parameters θ⟶, as shown in(5)aA=πAs,θ⟶.The selection of behavioraE obeys the Boltzmann-Gibbs probability distribution of(6)φa=1Zexp−εaEKBT.In (6), εaE=aE−aA2, when T is greater than 0, it is the thermodynamic temperature, where the degree of exploration of the behavior is characterized, the higher the temperature is, the greater the degree of exploration is and vice versa [43, 44]. KB=1.38×10−23Js/K is the Boltzmann constant, exp−εaE/KBT is the Boltzmann factor, Z is the distribution function, and there is(7)Z=∫−∞+∞exp−εaEKBTdaE=KBTπ.The update of parameterθ⟶ consists of two parts: one is to realize Δθ⟶CB by cerebellum CB and the other is to realize Δθ⟶BG by basal ganglia BG, as shown in(8)θ⟶←θ⟶+ωΔθ⟶BG+1−ωΔθ⟶CB.The supervisory error is expressed by(9)Es=12πss−πAs2.The weight change of the CB part of the behavioral network cerebellum is shown in(10)Δθ⟶CB=−α∂∇Es=α∂as−aA∇θπAs.The weight update of the BG part of the behavioral network basal ganglia uses an approximate strategy gradient estimation algorithm shown in(11)Δθ⟶BG=αδaE−aA∇θπAs,where a>0 is the step parameter of learning behavior network learning, 0≤ω≤1 is used to weigh two kinds of gradient information in the learning process, one is related to the second evaluation signal (also called internal reward δ), another kind of gradient information is related to the supervision behavior error as−aA. When the internal evaluation is good δ>0, cognitive behavior is updated to the direction of exploration behavior and supervision behavior [45–47] and the exploration behavior becomes a sample of network learning; on the contrary, the update direction is opposite and behavioral network search is more suitable for environmental behavior. The update formula of behavioral network based on cerebellum-basal ganglia mechanism is shown in(12)θ⟶=θ⟶+αδωaE−as+as−aA∇θπAs.The update algorithm of supervised behavior network weight shows that the TD error adjusts the supervisory error information; that is, the evaluation type learning priority is higher than the supervised learning. In this case, the supervisor is only an exploration source for exploring learning. In special cases, whenω=1, the exploration behavior plays a major role, and finally, the effective recognition of the face image is realized.


## 2.2.1. Behavioral Cognitive Model Based on Cerebellum-Basal Ganglia Mechanism

A behavioral cognitive computing model is a combination of the cognitive learning mechanism of the basal ganglia with the cerebellum monitoring mechanism. This model is based on the organization of the cerebellum and basal ganglia are constructed and used for face images [27]. Facial recognition allows for accurate facial recognition [28].


## 2.2.2. Basic Principles of Operational Conditioning Related to the Basal Ganglia

Operating conditional reflexes emphasize the effect of operational outcomes on behavior [29]. The difference between operational learning of conditional reflection and supervised learning is that feedback information obtained from the environment is a signal of assessment and not a signal of error. There are three basic elements of learning control based on the principle of operational conditional reflection: behavior selection mechanism (selection behavior according to probability), evaluation mechanism, and orientation mechanism.


## 2.2.3. Structure of the Behavioral Cognitive Computing Model for Coordination of the Cerebellum and Basal Ganglia

According to the working mode of the cerebellum and basal ganglia, a CB-BC-Based Cognition Computational Module [30] is proposed to simulate the cerebellum and basal ganglia mechanism. The model uses the operational reflection condition as its main learning mechanism and adopts the supervised behavior-learning structure [31]. As shown in Figure 3, the ultimate goal of learning is to transfer the agent from the initial state s0 to the target state sd.Figure 3
Behavioral cognition model structure based on cerebellum-basal ganglia mechanism.In Figure3, BG denotes basal ganglia; CB denotes cerebellum; IO denotes inferior olive; SN denotes substantia nigra; and CF denotes climbing fiber. The behavioral network part is realized by the joint action of BG and CB [32]. The evaluation part is attributed to BG, the solid line is the data stream, the dotted line is the learning algorithm [33], and the coordination factor ω is used to calculate the composite behavior, by using the form of weighted sum shown in(1)a←ωaE+1−ωas.In (1), aE is the exploratory behavior, which is implemented by the behavioral network aA=πAs,θ⟶ combined with probabilistic behavior selection, and as is the supervised behavior, which is a form of traditional feedback controller [34]. Supervised behavior provides intentions and solutions for the behavioral network in the early stage of learning, avoiding the excessive blind search. In the learning process, the coordination factor takes the form of an exponential increase; that is, the supervisor is not involved in the entire closed-loop control process.


## 2.2.4. Face Image Recognition

The process of face image recognition using the behavioral cognitive computing model coordinated by the cerebellum and the basal ganglia is a learning process. The process continuously collects state information. The state information is the face image feature acquired in Section2.1. The current state is st and performs a behavior at; then, it is transferred to the next state st+1, to obtain the instant reward rt+1 and updates the behavior and evaluation network [35–39]. As time goes on, the learning system can continuously collect state and behavior information. Consequently, the training samples are obtained during the learning process, which is the facial image characteristic to be recognized. The specific implementation principle is as follows: the learning sample of the behavioral network learns according to the evaluation information. When the exploration behavior is performed and the system state is developed in a good direction, the behavioral network aA=πAs,θ⟶ tends to explore behavior. At the moment, the present state and the exploratory behavior become the learning samples of the network. This ensures that the network converges under the condition of multiple learning cycles, and the agent can find the proper behavior for himself through training on operating conditions. The evaluation network obtains sampling data by continuously collecting information on rewards and state transition and updates the evaluation network through time difference (ST) learning. The role of the initial supervisor is to reduce the search space and provide the sampling network information necessary for the learning process in the assessment network. This allows the learning system to ensure minimum standard performance and avoid adverse situations [40–42]. The following is a detailed behavioral cognitive computing model that is coordinated with the cerebellum and basal ganglia to implement the face image recognition process:(1) Basal Ganglia Evaluation Network. The evaluation value function Vs approximates the future reward discount and the estimate is shown in(2)Vst=Ert+1+γVst+1.Equation (2) is used to evaluate the behavior and is approximated by the network fs,w⟶. The evaluation information rt+1 and the evaluation value Vst+1 of the next-time state are used to estimate the evaluation information of the current state, and the TD error is used, as shown in(3)δt=rt+1+γVst+1−Vst.The evaluation network weight is updated andzt is the qualification trace for evaluating the network weight, by using the linear differential equation as in(4)zt=γλzt−1+∂Vst∂w⟶t.(2) Cerebellum-Basal Ganglia Behavioral Network. Behavioral strategy πAs is a state-to-behavior mapping that can be approximated by a network with parameters θ⟶, as shown in(5)aA=πAs,θ⟶.The selection of behavioraE obeys the Boltzmann-Gibbs probability distribution of(6)φa=1Zexp−εaEKBT.In (6), εaE=aE−aA2, when T is greater than 0, it is the thermodynamic temperature, where the degree of exploration of the behavior is characterized, the higher the temperature is, the greater the degree of exploration is and vice versa [43, 44]. KB=1.38×10−23Js/K is the Boltzmann constant, exp−εaE/KBT is the Boltzmann factor, Z is the distribution function, and there is(7)Z=∫−∞+∞exp−εaEKBTdaE=KBTπ.The update of parameterθ⟶ consists of two parts: one is to realize Δθ⟶CB by cerebellum CB and the other is to realize Δθ⟶BG by basal ganglia BG, as shown in(8)θ⟶←θ⟶+ωΔθ⟶BG+1−ωΔθ⟶CB.The supervisory error is expressed by(9)Es=12πss−πAs2.The weight change of the CB part of the behavioral network cerebellum is shown in(10)Δθ⟶CB=−α∂∇Es=α∂as−aA∇θπAs.The weight update of the BG part of the behavioral network basal ganglia uses an approximate strategy gradient estimation algorithm shown in(11)Δθ⟶BG=αδaE−aA∇θπAs,where a>0 is the step parameter of learning behavior network learning, 0≤ω≤1 is used to weigh two kinds of gradient information in the learning process, one is related to the second evaluation signal (also called internal reward δ), another kind of gradient information is related to the supervision behavior error as−aA. When the internal evaluation is good δ>0, cognitive behavior is updated to the direction of exploration behavior and supervision behavior [45–47] and the exploration behavior becomes a sample of network learning; on the contrary, the update direction is opposite and behavioral network search is more suitable for environmental behavior. The update formula of behavioral network based on cerebellum-basal ganglia mechanism is shown in(12)θ⟶=θ⟶+αδωaE−as+as−aA∇θπAs.The update algorithm of supervised behavior network weight shows that the TD error adjusts the supervisory error information; that is, the evaluation type learning priority is higher than the supervised learning. In this case, the supervisor is only an exploration source for exploring learning. In special cases, whenω=1, the exploration behavior plays a major role, and finally, the effective recognition of the face image is realized.


## 3. Results

### 3.1. Algorithm Recognition Rate

To test the efficiency of the recognition results by using the CBGM algorithm, AR face database and USPS handwritten database are used in the experiment. To check the robustness of the CBGM algorithm for abnormal pixel images, the AR database is camouflaged (wearing glasses and wearing a scarf). The face image subset is used as a test sample to complete the experiment. The platform used in the experiment is AMD Athlon (TM) 2 processor, 2.9 G frequency, and 2 G memory.


#### 3.1.1. AR Face Database

The 100 targets in the AR face database are selected as the experimental objects. The data includes various facial expressions and illumination changes, all images are cropped to 60∗ 43 pixels, and the image is downsampled to achieve image reduction. For the experiment, 1/2 downsampling is used and the data is normalized.Table1 lists the average recognition rate comparison results of the three face image recognition algorithms including the CBGM algorithm in the AR database [48–50]. As per the data shown in the table, the average recognition rate of the face image recognition algorithm based on NSCT and bionic mode is 73.7%, and that of the weighted modular FR algorithm based on the K-means clustering method is 82.5%. The improved CBGM algorithm demonstrates high robustness in the face image recognition procedure, reaching the highest recognition rate of 96.9%.Table 1
Evaluation of average recognition rates of three algorithms on AR database.
Experimental algorithmRecognition accuracy rate (%)Face recognition algorithm based on NSCT and bionic pattern73.70Weighted modular FR algorithm based onK-means clustering method82.50Proposed CBGM96.90The experiment is to highlight the accuracy of different algorithms for face image recognition. 10 images with higher similarity are selected from the 100 targets of the AR face database, and different algorithms are used to identify the local features of 10 images. The recognition results are as for Figure4.Figure 4
Results of face recognition using different algorithms.It can be seen from the analysis of the three curves in Figure4 that the recognition rates of local facial recognition results using three algorithms for the AR face database are fairly diverse. The CBGM recognition rate varies by more than 95%. The facial recognition algorithm based on NSCT and bionic mode has the lowest recognition rate for different samples of local facial features, varying between 70 and 75%. The weighted modular facial image recognition algorithm based on the K-means clustering method has a high recognition rate, ranging between 80 and 85%.


#### 3.1.2. USPS Handwritten Digit Library

In this experiment, the USPS handwritten digital library is selected as the experimental object. The database contains 0–9 total of 10 handwritten face images. 100 images are arbitrarily selected from each category as training samples. 100 images are as training samples and 100 images are as the test sample. The image size is 16∗ 16 pixels, and the data is normalized. Figure 5 shows the recognition results of 10 local facial features by three algorithms in the USPS database.Figure 5
Recognition results of 10 local facial features in three USPS databases.It can be seen from the analysis of the three curves in Figure5 that the recognition results of the three algorithms in the USPS handwritten digit library are quite different. The recognition curve of the CBGM algorithm is at the highest point, which indicates that the CBGM algorithm has a strong recognition effect in the face image recognition of the USPS database.


#### 3.1.3. Analysis of Recognition Results When Face Images Are Occluded

Based on the face recognition results of the above three algorithms in the AR database and the USPS database, the experiment is to additionally confirm the face recognition performance of the CBGM algorithm. Three algorithms are used to identify the three occluded faces shown in Figure6. The recognition result is shown in Figure 7.Figure 6
(a) The first facial occlusion. (b) Second kinds of facial occlusion. (c) Third kinds of facial occlusion.
(a)(b)(c)Figure 7
Recognition results of three algorithms with occlusion face images.We can see that the three algorithms allow us to identify the pictures of the occlusion face. The CBGM algorithm can efficiently identify the three occlusion images from Figure7, and the recognition rates of the three occlusion images are greater than 94%. The recognition rate of the techniques like NSCT (nonsubsampled contourlet transform) and bionic mode on facial image recognition algorithms is below 90%. The recognition rate of the weighted modular face recognition algorithm based on the K-means clustering method is the lowest. A comprehensive analysis of the above experimental results illustrates that the recognition rate of occlusion face image applied to the AR face database and USPS handwritten digital library by using the CBGM algorithm performs better than that of face image recognition algorithm based on NSCT and bionic mode and weighted modular face image recognition algorithm based on K-means clustering method.


### 3.2. Time-Consuming Analysis of Face Image Recognition

The experiment further compares the three algorithms in the face recognition of the AR face database and the USPS handwritten digit library. The test results are revealed in Tables2 and 3, respectively. To highlight and compare the recognition time differences of the three algorithms, the data in Tables 2 and 3 are redrawn using the line graphs of Figures 8 and 9.Table 2
Test results of face recognition using three algorithms in AR face database(s).
Test numberFace recognition algorithm based on NSCT and bionic patternWeighted modular face recognition algorithm based onK-means clustering methodProposed CBGM11.021.230.3521.081.020.4230.880.750.2540.960.860.3450.820.770.2460.861.240.3270.881.050.4180.900.950.4290.850.750.38100.920.800.37Average result0.920.940.35Table 3
Test results of three algorithms for face recognition in USPS handwritten numeral library(s).
Test numberFace recognition algorithm based on NSCT and bionic patternWeighted modular face recognition algorithm based onK-means clustering methodProposed CBGM12.882.051.5622.682.611.5432.572.571.3642.642.641.4452.552.561.4862.642.571.5672.592.641.4782.662.891.5092.572.731.52102.622.861.55Average result2.642.611.50Figure 8
A polygon map for face recognition using three algorithms in the AR face database.Figure 9
A polygon map of three algorithms for face recognition in USPS handwritten numeral library.It can be seen from the analysis of Table2 and Figure 8 that the recognition time of the AR face database is quite different between the three algorithms. The average result of FR algorithm based on NSCT and bionic pattern, weighted modular FR algorithm based on K-means clustering method, and CBGM algorithm as shown in Table 2 are 0.92, 0.94, and 0.35, respectively. From the time curve represented by the three algorithms in Figure 8, it is observed that the face image recognition algorithm based on NSCT and bionic mode and the weighted modular facial recognition algorithm based on K-mean clustering method have a large change in facial recognition time, and the stability of the algorithms is poor. At the same time, the recognition time of the two algorithms is much higher than that of the CBGM algorithm. The variation of the time curve of the CBGM algorithm is small, and the recognition time is short, which indicates that the CBGM algorithm has strong applicability in practical applications.It can be seen from the analysis of Table3 and Figure 9 that when the three algorithms identify the face image in the USPS handwritten database, the average result of the recognition time of the FR algorithm based on NSCT and bionic pattern, weighted modular FR algorithm based on K-means clustering method, and the CBGM algorithm is 2.64 s, 2.61 s, and 1.5 s, respectively. Therefore, the recognition time of the CBGM algorithm is shorter. The fluctuation of the curve is not obvious, which indicates that the CBGM algorithm has strong stability and high recognition efficiency. The other two curves can be used to understand the face recognition algorithm based on NSCT and bionic mode and the weighted modularization based on the K-means clustering method. The recognition time of the two face image recognition algorithms is higher than 2 s, and the algorithm recognition time varies greatly. Therefore, the recognition efficiency of the algorithm is high.When the face has an occlusion as shown in Figure6, the recognition time of the three algorithms is as shown in Table 4.Table 4
Three algorithms for face recognition using face occlusion(s).
Test numberFace recognition algorithm based on NSCT and bionic patternWeighted modular face recognition algorithm based onK-means clustering methodProposed CBGMPicture 1Picture 2Picture 3Picture 1Picture 2Picture 3Picture 1Picture 2Picture 315.566.255.676.247.237.262.352.462.7325.486.245.486.567.217.232.212.652.7535.326.575.266.577.267.422.262.582.8445.166.616.246.687.157.152.242.642.6855.246.256.356.737.347.262.322.572.8165.266.346.457.017.257.142.242.482.7275.326.246.576.487.167.232.212.642.7684.386.116.286.687.147.242.742.592.7295.686.246.216.767.357.262.752.482.46106.216.275.626.537.417.322.262.622.26Average result5.366.346.016.627.257.252.362.572.67The data in Table4 show that the recognition time of facial images is increased by three algorithms when the face image is partially masked. As shown in the table, the face image recognition algorithm based on NSCT and bionic mode and the weighted modular FR algorithm based on the K-means clustering method is changing over 6 s. The variation in recognition time of the CBGM algorithm varies from 2.21 s to 2.84 s, which indicates that the CBGM algorithm can also quickly recognize the face when the face is occluded.Based on the results of experimental analyses above, the facial image recognition algorithm based on the cerebellar-basal node mechanism has the benefits of a high recognition rate and rapid recognition. It can be widely applied across a wide range of practical applications.


## 3.1. Algorithm Recognition Rate

To test the efficiency of the recognition results by using the CBGM algorithm, AR face database and USPS handwritten database are used in the experiment. To check the robustness of the CBGM algorithm for abnormal pixel images, the AR database is camouflaged (wearing glasses and wearing a scarf). The face image subset is used as a test sample to complete the experiment. The platform used in the experiment is AMD Athlon (TM) 2 processor, 2.9 G frequency, and 2 G memory.


### 3.1.1. AR Face Database

The 100 targets in the AR face database are selected as the experimental objects. The data includes various facial expressions and illumination changes, all images are cropped to 60∗ 43 pixels, and the image is downsampled to achieve image reduction. For the experiment, 1/2 downsampling is used and the data is normalized.Table1 lists the average recognition rate comparison results of the three face image recognition algorithms including the CBGM algorithm in the AR database [48–50]. As per the data shown in the table, the average recognition rate of the face image recognition algorithm based on NSCT and bionic mode is 73.7%, and that of the weighted modular FR algorithm based on the K-means clustering method is 82.5%. The improved CBGM algorithm demonstrates high robustness in the face image recognition procedure, reaching the highest recognition rate of 96.9%.Table 1
Evaluation of average recognition rates of three algorithms on AR database.
Experimental algorithmRecognition accuracy rate (%)Face recognition algorithm based on NSCT and bionic pattern73.70Weighted modular FR algorithm based onK-means clustering method82.50Proposed CBGM96.90The experiment is to highlight the accuracy of different algorithms for face image recognition. 10 images with higher similarity are selected from the 100 targets of the AR face database, and different algorithms are used to identify the local features of 10 images. The recognition results are as for Figure4.Figure 4
Results of face recognition using different algorithms.It can be seen from the analysis of the three curves in Figure4 that the recognition rates of local facial recognition results using three algorithms for the AR face database are fairly diverse. The CBGM recognition rate varies by more than 95%. The facial recognition algorithm based on NSCT and bionic mode has the lowest recognition rate for different samples of local facial features, varying between 70 and 75%. The weighted modular facial image recognition algorithm based on the K-means clustering method has a high recognition rate, ranging between 80 and 85%.


### 3.1.2. USPS Handwritten Digit Library

In this experiment, the USPS handwritten digital library is selected as the experimental object. The database contains 0–9 total of 10 handwritten face images. 100 images are arbitrarily selected from each category as training samples. 100 images are as training samples and 100 images are as the test sample. The image size is 16∗ 16 pixels, and the data is normalized. Figure 5 shows the recognition results of 10 local facial features by three algorithms in the USPS database.Figure 5
Recognition results of 10 local facial features in three USPS databases.It can be seen from the analysis of the three curves in Figure5 that the recognition results of the three algorithms in the USPS handwritten digit library are quite different. The recognition curve of the CBGM algorithm is at the highest point, which indicates that the CBGM algorithm has a strong recognition effect in the face image recognition of the USPS database.


### 3.1.3. Analysis of Recognition Results When Face Images Are Occluded

Based on the face recognition results of the above three algorithms in the AR database and the USPS database, the experiment is to additionally confirm the face recognition performance of the CBGM algorithm. Three algorithms are used to identify the three occluded faces shown in Figure6. The recognition result is shown in Figure 7.Figure 6
(a) The first facial occlusion. (b) Second kinds of facial occlusion. (c) Third kinds of facial occlusion.
(a)(b)(c)Figure 7
Recognition results of three algorithms with occlusion face images.We can see that the three algorithms allow us to identify the pictures of the occlusion face. The CBGM algorithm can efficiently identify the three occlusion images from Figure7, and the recognition rates of the three occlusion images are greater than 94%. The recognition rate of the techniques like NSCT (nonsubsampled contourlet transform) and bionic mode on facial image recognition algorithms is below 90%. The recognition rate of the weighted modular face recognition algorithm based on the K-means clustering method is the lowest. A comprehensive analysis of the above experimental results illustrates that the recognition rate of occlusion face image applied to the AR face database and USPS handwritten digital library by using the CBGM algorithm performs better than that of face image recognition algorithm based on NSCT and bionic mode and weighted modular face image recognition algorithm based on K-means clustering method.


## 3.1.1. AR Face Database

The 100 targets in the AR face database are selected as the experimental objects. The data includes various facial expressions and illumination changes, all images are cropped to 60∗ 43 pixels, and the image is downsampled to achieve image reduction. For the experiment, 1/2 downsampling is used and the data is normalized.Table1 lists the average recognition rate comparison results of the three face image recognition algorithms including the CBGM algorithm in the AR database [48–50]. As per the data shown in the table, the average recognition rate of the face image recognition algorithm based on NSCT and bionic mode is 73.7%, and that of the weighted modular FR algorithm based on the K-means clustering method is 82.5%. The improved CBGM algorithm demonstrates high robustness in the face image recognition procedure, reaching the highest recognition rate of 96.9%.Table 1
Evaluation of average recognition rates of three algorithms on AR database.
Experimental algorithmRecognition accuracy rate (%)Face recognition algorithm based on NSCT and bionic pattern73.70Weighted modular FR algorithm based onK-means clustering method82.50Proposed CBGM96.90The experiment is to highlight the accuracy of different algorithms for face image recognition. 10 images with higher similarity are selected from the 100 targets of the AR face database, and different algorithms are used to identify the local features of 10 images. The recognition results are as for Figure4.Figure 4
Results of face recognition using different algorithms.It can be seen from the analysis of the three curves in Figure4 that the recognition rates of local facial recognition results using three algorithms for the AR face database are fairly diverse. The CBGM recognition rate varies by more than 95%. The facial recognition algorithm based on NSCT and bionic mode has the lowest recognition rate for different samples of local facial features, varying between 70 and 75%. The weighted modular facial image recognition algorithm based on the K-means clustering method has a high recognition rate, ranging between 80 and 85%.


## 3.1.2. USPS Handwritten Digit Library

In this experiment, the USPS handwritten digital library is selected as the experimental object. The database contains 0–9 total of 10 handwritten face images. 100 images are arbitrarily selected from each category as training samples. 100 images are as training samples and 100 images are as the test sample. The image size is 16∗ 16 pixels, and the data is normalized. Figure 5 shows the recognition results of 10 local facial features by three algorithms in the USPS database.Figure 5
Recognition results of 10 local facial features in three USPS databases.It can be seen from the analysis of the three curves in Figure5 that the recognition results of the three algorithms in the USPS handwritten digit library are quite different. The recognition curve of the CBGM algorithm is at the highest point, which indicates that the CBGM algorithm has a strong recognition effect in the face image recognition of the USPS database.


## 3.1.3. Analysis of Recognition Results When Face Images Are Occluded

Based on the face recognition results of the above three algorithms in the AR database and the USPS database, the experiment is to additionally confirm the face recognition performance of the CBGM algorithm. Three algorithms are used to identify the three occluded faces shown in Figure6. The recognition result is shown in Figure 7.Figure 6
(a) The first facial occlusion. (b) Second kinds of facial occlusion. (c) Third kinds of facial occlusion.
(a)(b)(c)Figure 7
Recognition results of three algorithms with occlusion face images.We can see that the three algorithms allow us to identify the pictures of the occlusion face. The CBGM algorithm can efficiently identify the three occlusion images from Figure7, and the recognition rates of the three occlusion images are greater than 94%. The recognition rate of the techniques like NSCT (nonsubsampled contourlet transform) and bionic mode on facial image recognition algorithms is below 90%. The recognition rate of the weighted modular face recognition algorithm based on the K-means clustering method is the lowest. A comprehensive analysis of the above experimental results illustrates that the recognition rate of occlusion face image applied to the AR face database and USPS handwritten digital library by using the CBGM algorithm performs better than that of face image recognition algorithm based on NSCT and bionic mode and weighted modular face image recognition algorithm based on K-means clustering method.


## 3.2. Time-Consuming Analysis of Face Image Recognition

The experiment further compares the three algorithms in the face recognition of the AR face database and the USPS handwritten digit library. The test results are revealed in Tables2 and 3, respectively. To highlight and compare the recognition time differences of the three algorithms, the data in Tables 2 and 3 are redrawn using the line graphs of Figures 8 and 9.Table 2
Test results of face recognition using three algorithms in AR face database(s).
Test numberFace recognition algorithm based on NSCT and bionic patternWeighted modular face recognition algorithm based onK-means clustering methodProposed CBGM11.021.230.3521.081.020.4230.880.750.2540.960.860.3450.820.770.2460.861.240.3270.881.050.4180.900.950.4290.850.750.38100.920.800.37Average result0.920.940.35Table 3
Test results of three algorithms for face recognition in USPS handwritten numeral library(s).
Test numberFace recognition algorithm based on NSCT and bionic patternWeighted modular face recognition algorithm based onK-means clustering methodProposed CBGM12.882.051.5622.682.611.5432.572.571.3642.642.641.4452.552.561.4862.642.571.5672.592.641.4782.662.891.5092.572.731.52102.622.861.55Average result2.642.611.50Figure 8
A polygon map for face recognition using three algorithms in the AR face database.Figure 9
A polygon map of three algorithms for face recognition in USPS handwritten numeral library.It can be seen from the analysis of Table2 and Figure 8 that the recognition time of the AR face database is quite different between the three algorithms. The average result of FR algorithm based on NSCT and bionic pattern, weighted modular FR algorithm based on K-means clustering method, and CBGM algorithm as shown in Table 2 are 0.92, 0.94, and 0.35, respectively. From the time curve represented by the three algorithms in Figure 8, it is observed that the face image recognition algorithm based on NSCT and bionic mode and the weighted modular facial recognition algorithm based on K-mean clustering method have a large change in facial recognition time, and the stability of the algorithms is poor. At the same time, the recognition time of the two algorithms is much higher than that of the CBGM algorithm. The variation of the time curve of the CBGM algorithm is small, and the recognition time is short, which indicates that the CBGM algorithm has strong applicability in practical applications.It can be seen from the analysis of Table3 and Figure 9 that when the three algorithms identify the face image in the USPS handwritten database, the average result of the recognition time of the FR algorithm based on NSCT and bionic pattern, weighted modular FR algorithm based on K-means clustering method, and the CBGM algorithm is 2.64 s, 2.61 s, and 1.5 s, respectively. Therefore, the recognition time of the CBGM algorithm is shorter. The fluctuation of the curve is not obvious, which indicates that the CBGM algorithm has strong stability and high recognition efficiency. The other two curves can be used to understand the face recognition algorithm based on NSCT and bionic mode and the weighted modularization based on the K-means clustering method. The recognition time of the two face image recognition algorithms is higher than 2 s, and the algorithm recognition time varies greatly. Therefore, the recognition efficiency of the algorithm is high.When the face has an occlusion as shown in Figure6, the recognition time of the three algorithms is as shown in Table 4.Table 4
Three algorithms for face recognition using face occlusion(s).
Test numberFace recognition algorithm based on NSCT and bionic patternWeighted modular face recognition algorithm based onK-means clustering methodProposed CBGMPicture 1Picture 2Picture 3Picture 1Picture 2Picture 3Picture 1Picture 2Picture 315.566.255.676.247.237.262.352.462.7325.486.245.486.567.217.232.212.652.7535.326.575.266.577.267.422.262.582.8445.166.616.246.687.157.152.242.642.6855.246.256.356.737.347.262.322.572.8165.266.346.457.017.257.142.242.482.7275.326.246.576.487.167.232.212.642.7684.386.116.286.687.147.242.742.592.7295.686.246.216.767.357.262.752.482.46106.216.275.626.537.417.322.262.622.26Average result5.366.346.016.627.257.252.362.572.67The data in Table4 show that the recognition time of facial images is increased by three algorithms when the face image is partially masked. As shown in the table, the face image recognition algorithm based on NSCT and bionic mode and the weighted modular FR algorithm based on the K-means clustering method is changing over 6 s. The variation in recognition time of the CBGM algorithm varies from 2.21 s to 2.84 s, which indicates that the CBGM algorithm can also quickly recognize the face when the face is occluded.Based on the results of experimental analyses above, the facial image recognition algorithm based on the cerebellar-basal node mechanism has the benefits of a high recognition rate and rapid recognition. It can be widely applied across a wide range of practical applications.


## 4. Discussion

Given the research content of the CBGM algorithm in this document, some suggestions for future facial image recognition are suggested:(1)
Feature extraction of face images:Based on the research results of the existing facial recognition algorithm, the extraction of the main feature of the face image has the greatest effect on the recognition of the face image. As a result, the accuracy of the facial image extraction is improved, and it is convenient to obtain more valuable identification information from the local features of the face. In the future, estimation of facial characteristics should be further refined. The influence of illumination on facial feature extraction and the influence of weakened microexpression of facial image recognition should be reduced.(2)
Reinforce the study of the mechanism of the cerebellar and basal ganglia:The cerebellum and the basal ganglia are coordinated in a behavioral, cognitive computing model. This improves the mechanism for simulation of the coordination of the central nervous system of the human brain. The operating condition learning method in the biological cognition process is adopted to design the evaluation mechanism, the behavior selection mechanism, the orientation mechanism, and the learning algorithm. These coordinate the cerebellum and basal ganglia to promote the wide application of the cerebellum-basal ganglia mechanism and achieve effective recognition of facial images.


## 5. Conclusions

This research examines the algorithm for the recognition of images based on the mechanism of the cerebellum-basal ganglion. By extracting the effective image of the face and creating a pattern of behavioral identification based on the cerebellum-ganglion mechanism, the image of the face is detected. The experimental results analysis validates that the enhanced CBGM algorithm can effectively recognize face images. The recognition rate of 100 AR facial images is as high as 96.9%. The high accuracy and recognition ability have been achieved as the recognition time of facial occlusion images change only from 2.21 s to 2.84 s. The effectiveness of the proposed work is verified using detailed test results. This is then compared with the conventional algorithms like FR algorithm based on NSCT and bionic pattern and weighted modular FR algorithm based onK-means clustering method. Based on experimental data, the improved CBGM algorithm can efficiently and quickly recognize facial images in the AR face database as well as the USPS handwritten digit library. The algorithm can quickly identify facial images with occlusion on the face, which means that the efficiency of the application of the algorithm is strong.



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*Source: 3688881-2021-06-22.xml*