Effect of face and ocular multimodal biometric systems on gender classification

IET BiometricsVolume 8, Issue 4 p. 243-248 Research ArticleFree Access Effect of face and ocular multimodal biometric systems on gender classification Maryam Eskandari, Maryam Eskandari Department of Computer and Software Engineering, Toros University, Mersin, TurkeySearch for more papers by this authorOmid Sharifi, Corresponding Author Omid Sharifi omid.sharifi@toros.edu.tr Department of Computer and Software Engineering, Toros University, Mersin, TurkeySearch for more papers by this author Maryam Eskandari, Maryam Eskandari Department of Computer and Software Engineering, Toros University, Mersin, TurkeySearch for more papers by this authorOmid Sharifi, Corresponding Author Omid Sharifi omid.sharifi@toros.edu.tr Department of Computer and Software Engineering, Toros University, Mersin, TurkeySearch for more papers by this author First published: 05 February 2019 https://doi.org/10.1049/iet-bmt.2018.5134Citations: 10AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract This study is concerned with analysing face–ocular multimodal biometric systems for a person gender prediction. Particularly, this is the first study considering fusion of face and ocular biometrics to predict gender of a person via a hybrid multimodal scheme. The authors aim to investigate the effect of multimodal biometric systems at score and feature-level fusion on gender classification. The implementation of uniform local binary pattern (ULBP) feature extractor is taken into account to extract the face and ocular texture information. This paper proposes to select the efficient feature sets of both modalities using a novel evolutionary algorithm called backtracking search algorithm (BSA). On the other hand, support vector machine (SVM) is applied for classification purpose using the fused face and ocular features and scores. The proposed scheme is validated using CASIA-Iris-Distance and MBGC multimodal biometric databases with consideration of a subject-disjoint training and testing evaluation. The achieved gender recognition demonstrates the superiority of the hybrid multimodal face–ocular scheme over unimodal face and ocular schemes implemented in this study for a subject gender prediction. 1 Introduction Gender classification is considered as one of the relatively dynamic and interesting concepts in the area of biometrics. Gender classification based on face, iris, and ocular images has received much research interest in image analysis and pattern recognition problems [1–10]. Generally, in order to perform gender classification different types of feature extraction, selection, and classifiers have been applied. Gender recognition from face and iris unimodal biometrics is potentially considered as useful problem due to their functionalities and importance in applications of intelligent user interface, visual surveillance, collecting demographic statistics for marketing etc. Gender information extracted from face and ocular biometrics can be used to narrow down the average search time of person identification. Gender classification using face information has been applied in many applications in the last two decades. In [1], the gender of individuals was classified using face images. They applied SVM with the Radial Basic Function (RBF) kernel for classifying the gender of individuals. The authors of [2] reported the superiority of SVM for gender classification of face images over other classifiers such as neural networks and Adaboost. In [3], back propagation neural networks (BPN) was performed to classify the gender from face images. In order to improve the gender classification of face images, the authors of [4, 5] applied feature selection based on mutual information and feature fusion. In order to select the features, they fused three groups of features, three spatial scales, and four different mutual information measures. On the other hand, gender classification based on ocular images is a relatively new topic. The first work done on gender classification using iris information was reported in [6]. The authors reported the gender accuracy close to 80% using log-Gabor features and seven geometric features of the pupil and iris. The authors of [7] classified the gender of individuals according to their ocular information by extracting different textural descriptors and using combination of support vector machines (SVM) and multi-layer perceptron algorithms. In [8], the significant iris features were extracted using a statistical feature extraction technique based on correlation between adjacent pixels, and they were combined with a 2D wavelet tree on a small database without person-disjoint training. The authors of [9], investigated gender classification from iris images using different implementations of LBP. They reported significant improvement of gender prediction using Uniform LBP with concatenated histograms compared to whole iris image. In [10], the gender of individuals predicted directly from the same binary iris code that could be used for recognition. They achieved 89% correct gender prediction using the fusion of the best features of left and right iris codes on a person-disjoint training and testing dataset. In general, unimodal biometrics deals with single source biometric traits that suffer from limitations such as lack of uniqueness, non-universality, and noisy data [11]. Further research in biometrics proposed multimodal biometrics as a remedy to solve limitations of unimodal biometrics by combining information of multiple biometric traits. Our investigation on gender prediction under multimodality demonstrated the use of score-level fusion for fingerprint, palmprint, and hand geometry [12]. The authors extracted gender features from the samples separately and then combined them to minimise the searching time taken during the matching phase. In [13], a novel multimodal gender recognition system has been proposed to combine the head and mouth motion information with facial appearance using score-level fusion. They found that information extracted from video sequences can be considered as key strategy to develop accurate recognition systems. To best of our knowledge, there is not any other multimodal biometric systems especially face–ocular multi-biometrics to analyse the effect of fusion and data integration on gender classification. Multimodal biometric systems are commonly more robust and secure than unimodal biometric systems in terms of identification and verification. This paper aims to study the impact of multimodal biometric systems at score- and feature-level fusion through face and ocular biometric traits for gender prediction of individuals. We extract the facial and ocular texture features using ULBP [14] feature extraction method in this study. The paper proposes to select the efficient feature sets of both modalities using BSA [15] evolutionary algorithm and subsequently SVM [16] is applied to classify the individuals' gender using the fused face and ocular features and scores. In order to measure the robustness of proposed scheme for gender prediction, two face–ocular multimodal biometric databases namely CASIA-Iris-Distance [17] and Multiple Biometric Grand Challenge (MBGC) [18] with a subject-disjoint training and testing evaluation are considered in this study. The contribution of the present work is to design a robust face–ocular multimodal biometric system to predict the gender of individuals. This system can reduce the average of search time for a match based on the gender in searching an enrolled database better and more accurate than the systems using information of one modality. The implemented multimodal biometric system can also be used in marketing research applications where products are offered according to the individual's gender. In addition, the proposed face–ocular multimodal biometric systems can provide more significant information for high-security scenarios that need to determine the individuals' gender of unrecognised attempts. The main novelties of the present work consist on designing a new high-accuracy multimodal biometric scheme using face and ocular biometric traits and improving gender recognition performance using efficient feature subsets of modalities under person-disjoint evaluation. The rest of paper is organised as follows. Section 2 describes the structure of feature extraction method for unimodal face and ocular biometric systems; Section 3 indicates the implementation of multimodal biometric scheme for gender prediction of individuals. The detail of experimental results including the database description and assessment protocols is reported in Section 4, while Section 5 concludes this work. 2 Unimodal biometric systems The architecture of unimodal biometric systems is designed using face and ocular complementary biometric traits in this work. Generally, the processing steps of unimodal face and ocular biometric systems involve preprocessing, feature extraction, and recognition. The preprocessing step includes detecting face and ocular region images of individuals. In this study, some of face images are detected and aligned according to centre position of left and right irises. All the face images are then resized to 60 × 60. On the other hand, in preprocessing step of ocular unimodal, the circular iris and pupil region, occluding eyelids and eyelashes, and reflections are localised and detected. The detected region is then normalised into a fixed rectangular block of size 20 × 240 in this study. We also apply Histogram Equalisation (HE) and Mean Variance Normalisation (MVN) to reduce the effect of illumination in images. The feature extraction step for face and ocular biometric traits considers implementation of LBP feature extractor to describe the local spatial structure of images. LBP is a grey-scale texture operator introduced by Ojala et al. and is commonly used for face analysis [14]. It is considered as a powerful subpattern-based texture descriptor to represent efficient patterns. This feature extractor divides the image into several blocks to extract the local features. In order to extract the information related to the patterns on a set of pixels in each block, a histogram is applied and then the local extracted features are concatenated to provide a single global feature vector. LBP feature extractor computes a binary pattern by comparing its value with those of its neighbour according to a central pixel in an image. The first version of LBP extracted the features using a 3 × 3 window size that may prevent capturing large-scale structures. Later the extended version of original LBP such as uniform local binary pattern (ULBP) [19] was generalised to solve the problem of basic LBP by implementing the operator to circular neighbourhood with a different radius size. ULBP produces a separate output label for each uniform pattern of mapping, and for non-uniform patterns, a single output label is considered. Recently, applying ULBP feature extractor in [9] achieved high correct gender prediction using iris textures. In this study, we aim to apply ULBP feature extractor (ULBP (8, 1)) to extract the face and ocular region features prior to fusion in order to improve the correct gender prediction of face–ocular multimodal system. Therefore, we divide the face images into 36 sub-regions of 10 × 10 window size without and with overlapping of sub-regions here. On the other hand, the ocular regions are divided into 48 sub-regions of 10 × 10 window size without and with overlapping of sub-regions. This study performs 50% overlapping on windows of 10 × 10 size due to reporting high classification accuracy of individuals' gender [9]. The total number of features after histogram concatenation without overlapping for each face image is set to 36 regions × 59 bins = 2124 and for each ocular image to 48 regions × 59 bins = 2832. On the other hand, overlapping leads to increasing the number of sub-windows and subsequently features to 156 regions × 59 bins = 9204 for each face image and 188 regions × 59 bins = 11,092 for each ocular features. In order to reduce the effect of redundancy and high computational time of overlapping, we apply BSA feature selection on each sub-region of face and ocular images in this study. BSA selects 60% of computed histograms over the sub-windows before concatenation; the total number of features is then reduced to 5522 and 6655 for face and ocular images, respectively. It should be stated that parameter tuning, selection of sub-region, window size, and selection of features are performed experimentally using FERET [20], UBIRIS [21], ORL [22], and BANCA [23] face–ocular datasets with total number of 560 images in this study. The experiments performed in Section 4 demonstrate the improvement of gender classification under ULBP + Overlapping + BSA. Finally, the last processing step of face and ocular biometric systems in this study considers the classification of individuals' gender according to the extracted features and produced scores of the modalities using SVM classifier with RBF kernel. The extracted features and produced matching scores of unimodal face and ocular region are used later in proposed multimodal biometric scheme to predict the gender of subjects. The Manhattan distance measurement is applied here to produce the matching scores of each unimodal system. 3 Multimodal biometric systems Multimodal biometric systems extract information from multiple biometric resources in order to solve limitations raised by unimodal biometrics. The fusion of information for this kind of system is done at four different levels such as sensor level, match score level, feature level, and decision level fusion [24]. This study takes into account the consideration of match score level and feature level fusion to predicate gender of individuals due to the ease in accessing, fusing and providing high recognition performance [25–33]. Match score level fusion considers three different categories to fuse the scores, namely transformation-based score fusion, classifier-based score fusion, and density-based score fusion. In this project, SVM strategy is applied for combining the produced Manhattan distance scores of face, bio-ocular regions (left and right), and classification to get the results of subjects' gender decisions. SVM is able to obtain superior results in numerous classification and recognition problems by providing better generalisation performance [34–37]. SVM algorithm is a predictive method to pinpoint a class to which input belonged. Thus, SVM training algorithm attempts to construct a model in order to assign new sample to a category by setting a set of training samples that are labelled and provided for it previously. In order to construct SVM, different kernels are available. Generally, polynomial, linear, and radial basis kernel functions (RBF) are considered as most common kernel function for SVM. In this study, RBF kernel is applied for experimentation. In feature level fusion, the concatenation of feature sets leads to high dimensionality and redundant data and consequently affects the recognition performance [38]. In order to reduce the effect of dimensionality and redundancy in this level of fusion, we applied BSA feature selection strategy. Feature selection algorithms select an optimal subset of original feature sets based on a certain objective function to alleviate the dimensionality and redundancy problem. BSA is a new evolutionary algorithm introduced by Civicioglu [15] for solving numerical optimisation problems by searching local and global optimum. The algorithm considers a single parameter, a simple, effective, and fast structure to solve multimodal problems. The BSA memory leads to generating trial populations using previous-generation experiences. This feature selection algorithm contains five process steps, namely, initialisation, selection-I, mutation, crossover, and selection-II. In this study, we apply gender classification rate (GCR) (1) as the objective function to be maximised. (1) On the other hand, a binary bit string of length M consisting of '0' and '1' is used to select the optimised feature sets. The value of M indicates the number of features, '0' means the feature is not selected and '1' means the feature is selected. In order to set the parameters of BSA feature selection technique, training data set is used in this study. Therefore, BSA parameters such as population size, iteration, F and mix-rate are estimated using this set to attain the optimal feature sets. In this work, we set F = 1, mix-rate = 1, population and iteration size are both set to 30. Our multimodal biometric system for gender classification includes a hybrid scheme to fuse the feature and matching scores of face and bio-ocular regions. 3.1 Proposed multimodal biometric system for gender prediction The proposed scheme attempts to combine feature and score level fusions to explore the effect of these two fusion levels of face and bio-ocular regions on gender prediction of individuals. Fig. 1 predicts the block diagram of proposed scheme for gender classification. As the figure demonstrates, the features of all modalities are extracted using ULBP feature extractor under 50% overlapping of each block and 60% selection of computed histogram of each sub-window. The extracted optimised features are then concatenated in order to fuse face and bio-ocular information of the individuals in feature level fusion. Figure 1Open in figure viewerPowerPoint Block diagram of the proposed multimodal biometric scheme for gender classification The BSA feature selection strategy is applied after concatenation of all modality histograms one time more in this proposed scheme to reduce the effect of high dimensionality and computational time and subsequently improving the classification rate. On the other hand, we fuse the produced scores of each modality together with the produced scores of feature level fusion at this scheme via SVM classifier to combine the advantages of both levels in order to achieve a robust multimodal biometric system for gender prediction of subjects. Indeed, SVM combines the produced matching scores of modalities into a feature vector and then classifies them to get the results of subjects' gender decisions. The experiments done in Section 4 show the superiority of the proposed scheme to predict the gender of individuals under person-disjoint evaluation. 4 Experimental results We perform two set of experiments on CASIA-Iris-Distance and MBGC multimodal biometric databases in this study. The first set of experiments is done using CASIA-Iris-Distance database. This database includes both dual-eye iris and face patterns of individuals and the full database contains images of 142 subjects captured by a high-resolution camera and therefore suitable for multimodal biometric information fusion [17]. In order to perform the experiments, we randomly selected face and bio-ocular images of 100 individuals containing 50 males and 50 females. For each individual, only one image of face, bio-ocular (left and right) regions was selected to construct the database of total 300 images. The second set of experiments is then performed using MBGC multimodal biometric databases. Generally, the aim of this database is to improve the performance of face and ocular recognition under unconstrained conditions using three challenge problems. In order to perform the experiments, we constructed a dataset of face–ocular images of this database using face and bio-ocular images of 135 subjects including 75 males and 60 females. For each subject, only one image of face, left and right ocular regions was selected to form the dataset of total 405 images. Here, in order to perform the experiments, we assign 50% of CASIA-Iris-Distance dataset randomly to training and 50% to testing sets for males and females, respectively. Furthermore, the division of face and ocular datasets to training and testing was repeated 10 times, in order to create different random associations of face and ocular images. The result is averaged over 10 different runs and reported as mean and standard deviations. On the other hand, in MBGC face–ocular dataset the construction of training portion of dataset is done by random selection of 80% males and females while test portion considers the remaining 20% of males and females. The final gender recognition is reported as mean and standard deviations over 10 trials. The experiments are conducted separately for unimodal and multimodal biometric systems to investigate their affection on gender prediction of individuals. The unimodal biometric system considers face, left, right, and fusion of left and right ocular regions. Table 1 demonstrates gender classification rate of CASIA-Iris-Distance dataset using SVM classifier when face, left, right, and combination of both ocular features are used. The reported experimental results in Table 1 demonstrates the highest gender classification rate using ULBP, the overlapping histograms and BSA feature selection for face and both ocular regions with and without combination. The best gender classification performance for CASIA-Iris-Distance dataset has been obtained using male face images as 84%. The combination of left and right ocular regions improved the gender classification performance for both male and female gender classification compared to using only one of the modalities (left or right) for classification. The best correct classification rate of feature combination of bio-ocular regions in this database is 80% for males and it has 12% improvement over using only left region for classification. Table 1. Gender classification rate using unimodal systems features for CASIA-Iris-Distance dataset Method Unimodal biometrics Face, % Male, % Female, % Left ocular, % Male, % Female, % Right ocular, % Male, % Female, % Left + right, % Male, % Female, % ULBP 72.00 ± 0.71 76.00 68.00 58.00 ± 0.93 60.00 56.00 48.00 ± 0.80 52.00 44.00 66.00 ± 0.75 68.00 64.00 ULBP + OV 76.00 ± 0.35 80.00 72.00 64.00 ± 0.66 64.00 64.00 54.00 ± 0.50 56.00 52.00 70.00 ± 0.62 72.00 68.00 ULBP + OV + BSA 82.00 ± 0.82 84.00 80.00 66.00 ± 0.75 68.00 64.00 62.00 ± 0.70 60.00 64.00 78.00 ± 0.91 80.00 76.00 On the other hand, Table 2 analyses the gender classification of MBGC dataset using the same methods. The best correct gender classification using unimodal biometric system in this dataset is achieved when ULBP, the overlapping histograms and BSA feature selection methods applied on female face images as 83.34%. The improvement of gender classification performance from ocular biometric is achieved up to 75% for females when the ocular patterns of both left and right regions is used. In fact, the compression of Tables 1 and 2 demonstrates the capability of fusion, even for one biometric trait, in gender classification performance. Although, the highest prediction rate in both tables belongs to face unimodal biometric but investigating the table columns shows the superiority of fusion of both ocular features over the prediction using only left or right region. As an example, the correct gender classification rate of females in Table 2 after applying ULBP + OV + BSA for right and left ocular is 66.67%, while after left and right ocular fusion, this performance is improved to 75%. In this respect, therefore, according to the results obtained for combination of both ocular regions in Tables 1 and 2, we are motivated to apply fusion of face and both ocular regions for gender prediction of individuals in feature and score level fusion on both datasets. Table 3 investigates the effect of multimodality on gender classification of CASIA-Iris-Distance dataset. The table considers two different kinds of experiments for fusion of features and scores of face, left and right oculars, and classification of genders. In feature level fusion, the features of face, left and right oculars are considered as the input of SVM classifier to be combined and then SVM classifies the genders of combined features. In score level fusion, the produced face, left and right ocular scores by Manhattan distance measurement are considered as the input of SVM classifier to be fused and then gender classification is done. Table 2. Gender classification rate using unimodal systems features for MBGC dataset Method Unimodal biometrics Face, % Male, % Female, % Left ocular, % Male, % Female, % Right ocular, % Male, % Female, % Left + right, % Male, % Female, % ULBP 70.84 ± 0.34 66.67 75.00 51.67 ± 0.53 53.34 50.00 55.00 ± 0.65 60.00 50.00 62.20 ± 0.40 66.67 58.34 ULBP + OV 74.17 ± 0.21 73.34 75.00 59.17 ± 0.60 60.00 58.34 62.20 ± 0.40 66.67 58.34 70.00 ± 0.56 73.34 66.67 ULBP + OV + BSA 81.67 ± 1.03 80.00 83.34 66.67 ± 0.65 66.67 66.67 66.67 ± 0.73 66.67 66.67 74.17 ± 0.35 73.34 75.00 Table 3. Gender classification rate using multimodal biometric systems for CASIA-Iris-Distance dataset Method Feature level fusion Score level fusion Proposed method Face + left + right Male Female Face + left + right Male Female Overall prediction ULBP 74.00 ± 0.88 76.00 72.00 78.00 ± 1.01 76.00 80.00 88.00 ± 0.94 ULBP + OV 78.00 ± 0.65 80.00 76.00 82.00 ± 0.83 84.00 80.00 Male Female ULBP + OV + BSA 83.00 ± 0.70 84.00 82.00 84.00 ± 0.96 84.00 84.00 92.00 84.00 As reported in Table 3, the best correct classification rate obtained using the proposed method for males when combination of feature and score level fusion are considered to construct the multimodal biometric systems for gender classification as 92%. Indeed, analysing the results of Table 3 shows the strength of multimodal biometric systems for gender prediction of individuals. Score level fusion for this dataset is able to obtain the better prediction rate for both males and females compared to feature level fusion. In feature level fusion, the concatenation of ULBP feature sets of modalities leads to high dimensionality and redundant data and consequently affects the prediction rate. The effect of dimensionality and redundancy is then alleviated after applying BSA feature selection. Finally, the proposed method combines the advantage of feature and score level fusion together. In the continuation of this study, we repeated the experiments for multimodal biometric system on MBGC dataset in Table 4. Table 4. Gender classification rate using multimodal biometric systems for MBGC dataset Method Feature level fusion Score level fusion Proposed method Face + left + right Male Female Face + left + right Male Female Overall prediction ULBP 74.17 ± 0.50 73.34 75.00 77.50 ± 0.33 80.00 75.00 92.51 ± 0.68 ULBP + OV 81.67 ± 0.40 80.00 83.34 81.67 ± 0.45 80.00 83.34 Male Female ULBP + OV + BSA 89.17 ± 0.75 86.67 91.67 89.17 ± 0.92 86.67 91.67 93.34 91.67 The proposed method achieved 92.51% improvement on overall gender prediction rate for MBGC dataset. Additionally, the best gender classification rate belongs to males as 93.34%. The results of score and feature level fusion in this table outperform unimodal biometric experimental results reported in Table 2. In fact, the hybrid method presented in the proposed scheme of this study outperforms all implemented unimodal and multimodal biometric systems for gender prediction of individuals. The high prediction performance for our subject-disjoint datasets is due to gathering the strength of each unimodal biometric system into a hybrid multimodal system. Consequently removing the redundant data and combining the advantage of each level of fusion in this study is provided in the proposed scheme to be used in high security scenarios that need to determine the individuals' gender of unrecognised attempts. On the other hand, in order to demonstrate the effectiveness of multimodality and the proposed scheme, a comparison is presented with state-of-the-art fusion sets on CASIA-Iris-Distance dataset. As reported in Table 5, the fusion sets applied in thisstudy improved the gender prediction rate compared to unimodal methods applied inTable 1. All the fusion sets combine face, left ocular and right ocularfeatures, scores or decisions using ULBP + Overlapping + BSA. Therefore, in scorelevel fusion, the scores obtained using above-mentioned modalities are combinedusing Min, Max, Weighted Sum, and Product rules. Multiple Kernel Learning methoduses the concatenated features of modalities to represent and discriminate thesamples more efficient. Decision level fusion here combines the decisions achievedby each modality using Majority Voting, OR, AND, Threshold Optimised OR, andThreshold Optimized AND rules. Analysing the gender prediction rate of Table 5 indicates the recognition improvement of ourproposed scheme over other fusion sets implemented in this study. The proposedmethod achieved 2% improvement over the best gender prediction using ThresholdOptimised OR Rule and Multiple kernel learning methods. Additionally, in order tocompare the techniques implemented in the work more statistically, we reported thebest result achieved using unimodal system, score level fusion, feature level fusiontogether with proposed scheme in Table 6 interm of total error rate (TER). TER is equal to twice the value of equal error rate(EER) in one biometric system. Table 5. Comparison of proposed method with State-of-the-art fusion techniques onCASIA-Iris-Distance dataset Fusion techniques Prediction rate Score level fusion min rule [28] 83.00 ± 0.39 max rule [28] 83.00 ± 0.90 weighted sum rule [13] 85.00 ± 0.50 product rule [25] 85.00 ± 0.74 Feature level fusion multiple kernel learning [39] 86.00 ± 0.64 Decision level fusion AND rule [33] 83.00 ± 0.81 majority voting (MV) [40] 83.00 ± 0.73 OR rule [33] 84.00 ± 0.52 threshold optimised AND rule [41] 85.00 ± 0.57 threshold optimised OR rule [41] 86.00 ± 0.66 Proposed scheme 88.00 ± 0.94 Table 6. Total error rate of different techniques on CASIA-iris-distancedataset Methods Total error rate(TER) unimodal system 8.38 ± 0.63 feature level fusion 8.18 ± 0.59 score level fusion 7.69 ± 0.83 proposed scheme 5.85 ± 0.40 The TER of face biometric is presented for unimodal system in Table 6 since the best performance is obtained using this modality in Table 1. Comparison of TER of Table 6 shows the superiority of multimodality and specially the proposed scheme for gender classification of individuals. However, the detail investigation of results shows that generally score level fusion is better than feature level fusion in term of TER. Additionally, in order to evaluate the computation time of the proposed method and alsoconsidering combination of texture and geometric features (GF) of irises [6, 42] inthe proposed multimodal system, we performed a new experiments onCASIA-Iris-Distance dataset. The experimental results done in Table 7 show that applying only GF approach leads tolower prediction rate compared to the time that textures are used (Table 1). However, combining the textures of face, ocular and GF's of iris inour multimodal biometric system is able to improve the gender prediction ofindividuals in this study as 90% with computational time of 4 s. Table 7. Gender prediction rate and execution time of proposed method using irisgeometric features on CASIA-iris-distance database Method Prediction rate Execution time GF-left 64.00 ± 0.50 0.36 GF-right 54.00 ± 0.45 0.36 GF-left + right 66.00 ± 0.62 0.38 proposed method without GF 88.00 ± 0.40 4.00 proposed method with GF 90.00 ± 0.73 4.00 5 Conclusion This study is the first to explore the effect of multimodal biometric systems using face and ocular region of individuals on gender prediction. We investigated the effect of multimodal biometric systems especially at score and feature level fusion on gender classification. ULBP feature extractor as a strong technique for face unimodal and ocular unimodal biometric systems along with overlapping of histograms and BSA feature selection strategy was applied to extract the optimal features of modalities. Then, the extracted optimal features of the modalities were combined in feature and score level fusion separately for testing the subject-disjoint datasets in this study. The experimental results reported in the work were shown in the effectiveness of multimodality and information fusion for these two levels of fusion on gender classification. The ability of multimodality was demonstrated here by improving the prediction rate of individuals' gender and introducing a hybrid fusion system. The proposed fusion scheme combined the advantages of unimodal and multimodal biometric systems to specify the individuals' gender of unrecognised attempts that can be used for several high-security scenarios. 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