## 2 Literature Review
Further, the authors of [49] identified nine facial features that varied and hence could be used to distinguish males from females, namely, the hairline, eyebrows, eyes, distance between the eye and eyebrows, nose, lips, chin, cheeks, and face shape. The hand-crafted features could be extracted from the facial features including the face shape by using the histogram of oriented gradients (HOGs) [50], texture using the local binary pattern (LBP) [51], and intensity features using the gray level of each pixel [20]. The geometric features can also be extracted, such as scale invariant feature transform (SIFT) [52] and Haar-Like features [21]. [PERSON] et al. [19] presented face images using a novel texture descriptor local directional pattern (LDP), and [PERSON] and [PERSON] [10] proposed interlaced derivative patterns, which outperformed the LBP and LDP features. A number of authors have reported performance improvements when different types of hand-crafted features are fused, such as domain-specific and trainable features [18], trainable shapes, and color features [53], LBP and local phase quantization features [8], shape and texture features [54], LBP and radii spatial scales features [20], appearance-based and geometric-based features [55], appearance and geometry features [12], gradient and Gabor wavelets features [13], and LBP, SIFT, and color histogram [52]. In contrast, [PERSON] [11] showed that a single feature from different scales could outperform multiple features at a single scale. In [9], adaptive features were proposed, which resulted in accuracy improvements in the SVM model. The research in [31] showed that the hand-crafted features fusion could improve the SVM performance. A growing body of literature has investigated deep-learned features and investigated how gender recognition accuracy differs when compared and combined with hand-crafted features. [PERSON] et al. [29] proposed a generic computer vision system that extracted, compared, and combined hand-crafted features with deep-learned features to train an SVM model using several datasets from different domains. The authors showed that a fusion of both hand-crafted and deep-learned features provided the best performance with SVM. [PERSON] et al. [34] explored transfer learning using generic and domain-specific models to extract deep-learned features to train different CNN and SVM models. Their results proved that the use of deep-learned features extracted using domain-specific models could improve the accuracy of all the models. In [56], the authors proposed the joint features learning deep neural networks, which could learn from the joint high-level and low-level features. The proposed architecture outperformed CNNs, SVM with face pixels, and SVM with LBP features. In [35], the authors compared hand-crafted and deep-learned features by training a CNN model for pedestrian gender recognition. Their results showed that hand-crafted and deep-learned features performed comparably on small-sized homogenous datasets, but the latter performed significantly better on heterogeneous data. In [57], the authors showed that feeding deep-learned features into an SVM rather than Softmax in VGGNet-16 provided better results. The fusion of deep-learned and hand-crafted features achieved better results than using only deep-learned features with ensemble learning [58]. SVM is a widely used model in the gender recognition domain [9; 17; 19; 20; 21; 25; 43; 47; 54; 59; 60]. Lately, deep learning is being used in many computer vision applications [61; 62; 63; 64]. Therefore, studies have proposed varying architectures and experimental setups for CNNs to improve gender recognition [5; 14; 16; 21; 23; 24; 30; 40; 44; 49; 62; 65; 66; 67; 68; 69; 70]. Other authors have used ensemble learning [58] and K-nearest neighbor (KNN) [63] methods. The studies [21; 32; 33; 34; 37; 38] are most similar to our study, wherein the main aim is to compare the performances of different features and machine learning models for the task of gender recognition.