# Deep-Learning-Based Cognitive Assistance Embedded Systems for People with Visual Impairment
## 2 Related Work
### Gender, Age, and Emotion Classifications  
Gender classification can be performed by evaluating a person's ear, fingerprint, iris, voice, or face. Face-based gender classification is the most common gender classification method. [PERSON] et al. proposed a real-time fully connected CNN called mini-Xception for gender and emotion classifications [42]. Their model includes convolutional layers, the rectified linear unit (ReLU) activation function, a residual depth-wise separable convolution structure, the softmax activation function, and an average pooling layer. The residual depth-wise separable convolution structure contains two layers: a depth-wise layer and a point-wise layer. [PERSON] et al. proposed a CNN model for face-based gender classification [43]. This model has a simple architecture that comprises only three convolutional layers and one output layer. Cross-correlation was used instead of convolution to reduce the computational load. [PERSON] et al. [44] proposed a compact soft stagewise regression network (SSR-Net) for age categorization; they later extended SSR-Net to perform gender classification [45]. The improved model has two heterogeneous streams, each of which comprises \(3\times 3\) convolutional layers, batch normalization layers, nonlinear activation layers, and \(2\times 2\) pooling layers. [PERSON] et al. [46] demonstrated a CNN model for face-based gender classification based on the VGGNet [47] architecture. This model can automatically extract features from images without using the HOG or support vector machine method because it uses a deep CNN. [PERSON] et al. [48] suggested a system for automatic gender categorization. They first trained a segmentation model by using a conditional random field. The model then divided a facial image into six parts: eyes, hair, mouth, nose, skin, and background. A probability map was subsequently produced for each of the six classes by using a probabilistic classification strategy. These probability maps were then used for gender classification. Numerous studies have investigated age classification. Age and gender classifications or age and emotion classifications are frequently performed together in studies because these classifications are based on facial characteristics. [PERSON] and [PERSON] [49] presented a deep CNN model with an architecture inspired by AlexNet for identifying age and gender [50]; however, this model only has five main layers: three convolutional layers and two fully connected layers. It also has max-pooling, normalization, and dropout layers; uses the ReLU activation function; and includes filters. A face-based model for age and gender classifications was presented by [PERSON] and [PERSON] [51]. The network architecture of this model, which is also based on AlexNet, includes convolutional, ReLU activation, batch normalization, max-pooling, dropout, and fully connected layers. [PERSON] et al. [52] demonstrated a sophisticated CNN-based approach for age estimation. To create high-resolution face images from low-resolution images, a conditional generative adversarial network was used to produce images as the model's input. VGGNet was used for age estimation in [47]. [PERSON] et al. [53] developed a CNN model for estimating age according to the divide-and-rule strategy. A robust feature extraction technique involving the use of a deep CNN based on the GoogLeNet [54] architecture was used to extract face features, and a divide-and-rule learning approach was proposed for age estimation. [PERSON] et al. [55] presented a novel residual network based on ResNet [56] for age and gender classifications. They suggested two strategies for improving age estimation performance by observing the features of each age group. Face emotion classification is used in numerous real-world contexts and has been extensively discussed in the literature by many authors. [PERSON] et al. [57] proposed a new deep CNN model called the supervised scoring ensemble model for emotion recognition. An auxiliary block is the foundation of this model's architecture.