Gender classification provides additional information about the individual's identity, which is crucial in surveillance, smart interface, and smart advertising, it is a very important aspect of face analysis that has piqued the interest of researchers in areas such as demographic information collection, surveillance, human-computer interaction, marketing intelligence, security, etc. Usually, facial images are used to extract features for classification. This study aims to determine gender based on the entire face or the eyes (for masked or occlusion faces). The proposed method consists of six main stages: (1) Remove the background using the Deep Labelling Version 3 plus (DeepLabV3+) method; (2) Skin detection by applying the combination of the two colors spaces models (Hue, Saturation, Value (HSV)) and (Luminance, Chrominance blue and Chrominance red (YCbCr)); (3) Face detection using the Haar Cascades classifier method; (4) Face alignment and cropping; (5) Classify the gender. Classification of the gender when the image contains the entire face is based on a deep wavelet. In the case of occlusion faces, we proposed to use a Convolution Neural Network (CNN) for classifying the gender from the eye (s). The dataset used for training this model is the celeb faces attributes dataset (CelebA) and some of the different datasets were also used to compare this model with the other previous works. In addition, we showed that our models perform well with images with some challenges, for example, some of the faces are not fully visible, not completely frontal, wearing glasses with different styles, have low quality and noise, have various lighting, children's and infants' faces, with and without makeup, open and closed mouths and closed eyes. Other than that, the accuracy achieved was 98% when classifying from face images and 98% from the eye. The proposed method was compared with previous works and was very promising. The contributions of this proposal were the use of the deep wavelet for gender classification and the proposal of a new method for skin detection, which enhances the performance of the Haar cascade method used for face detection. Also, we proposed a method for face alignment and, finally, the classification of genders with many challenges from the entire face or just from the eye (s).

Due to the advancement of the methodologies employed in this field and the increased attention being paid to the deep learning (DL) techniques' implementation, focusing on convolutional neural networks (CNNs), gender and age estimates have recently assumed a significant amount of relevance. It is important to precisely predict the gender, including the age of a person, provided that it is used in many applications for smart devices, including those related to security, health, and other areas. Although there have been several studies and research in this area, gender, and age estimation still confront certain problems and difficulties, such as existing of earrings, races, masked faces, makeup, etc. which might interfere with the systems' operations and decrease their accuracy. In this paper, we assess the accuracy of the models employed in three of the most well-known datasets: MORPH2, FG-NET, and OUI-Adience. Our focus is on the best and most recent technology available in this field. Additionally, we have mentioned a list of most of the challenges that may face in the process of estimating age and gender, as well as a list of applications and areas in which it can be used.

Due to the numerous variances in face appearance, age estimation using facial images is a difficult subject. Many factors can affect the estimation of human age such as race, face post, gender, lifestyle, etc. By considering more factors, the optimum performance may be obtained. In this study, we proposed a method to predict the age of facial images. The proposed method consists of four main stages: (1) Preprocessing. (2) Face alignment and cropping. (3) Feature extraction by using Deep Wavelet Network (DWN). ( 4) Age prediction. Five of the machine learning classifiers (K-nearest neighbor, support vector machine, Naïve Bayes, decision tree, and random forest) were suggested in this proposal to combine with DWN and then select the best performance one. Two DWNs trained for male and female faces separately, so we have to classify faces before inputting to one of the two networks (classifying faces' gender is out of this study's scope). The performance of predicting the age was measured first when the age was divided into eleven age groups, where the accuracy was 97% for the females and 98% for the males. Also, we secondly measured the performance when the age was divided into seventeen age groups (five years for each group) with an accuracy of 91% for female and 92% for male faces.