## 2 Literature Review  
Gender recognition is a domain where high state-of-the-art accuracy has been achieved by SVMs and CNNs [21; 33; 34; 37; 38]. These results, however, have been attributed to the characteristics of the dataset used [17; 21; 22; 31]. For example, many of the early efforts in gender recognition have used constrained datasets that included frontal face images that were taken under controlled conditions of facial expressions, illumination, and background [19; 20; 21], and hence cannot achieve the same performance with images taken in the wild by surveillance or robot cameras. Building a gender recognition model based on face images is similar to other computer vision tasks; the process has three main stages: selecting the benchmark dataset, feature extraction and selection, and classification. In the text below, we highlight the main efforts made in each stage for progress in the field. Furthermore, we summarize the results of the most relevant works in Table 1.  
A dataset is an integral part of gender recognition research. Selecting an appropriate dataset to benchmark the proposed approach is a crucial decision because datasets introduce different challenges, such as pose variations, illumination variations, and occlusions. Gender recognition datasets can be broadly categorized into constrained and unconstrained datasets. The former includes frontal face images that were taken under controlled conditions of facial expressions, illumination, and background. Numerous early studies have been criticized for benchmarking their works with constrained datasets, such as FERET [19, 20, 21, 22] and UND [20] because they do not reflect real-world situations [23, 39]. Therefore, many studies were aimed at the challenges posed by the images taken under uncontrolled conditions, for example, LFW [20, 22] and Adience [17, 23, 32, 40, 41] datasets and datasets with occlusions (e.g., sunglasses and hats), such as AR [20, 22], Gallagher [32], and MORPH [40]. The authors in [17] offered a unique unconstrained and unfiltered dataset. [PERSON] and [PERSON] [42] argued that the most popular datasets were biased, and they emphasized that using a single dataset for training and testing was not representative of the variations that exist in the real world. Therefore, to simulate real-life situations, recent studies [17, 21, 22, 31] have adopted a cross-data approach, where one dataset is trained, and another dataset is tested. Other contributions [38, 41] used a fusion of multiple constrained and unconstrained datasets for testing purposes. Moreover, some efforts have targeted a specific type of image, such as low-resolution thumbnail faces [43] and low-frequency components of the mosaic \(8\times 8\) images [44]. A fundamental problem was to determine what features in a person's face can help determine the person's gender. A wide range of studies have been devoted to improving the extraction and selection of features [45, 46, 47]. In recent years, there has been an increasing amount of computer vision literature that distinguishes between hand-crafted features and deep-learned features [45]. The hand-crafted features are designed beforehand by human experts, whereas deep-learned features are learned directly from the data using CNN. Furthermore, some studies reported performance improvements when the two features were combined [31, 32]. One of the early works on the hand-crafted features is [48], where the authors combined the 3D distances with multiple measurements (such as the distance between the key points in the face, the ratios, and the angles between the key points) into a single function. [PERSON] et al. [44] divided the human face into four parts to determine which part contributed the most to identifying the gender. The results revealed that the face shape and cheek bone shape are the most important aspects.