Abstract
Many types of deep neural networks have been proposed to address the problem of human biometric identification, especially in the areas of face detection and recognition. Local deep neural networks have been recently used in face-based age and gender classification, despite their improvement in performance, their costs on model training is rather expensive. In this paper, we propose to construct a local deep neural network for age and gender classification. In our proposed model, local image patches are selected based on the detected facial landmarks; the selected patches are then used for the network training. A holistical edge map for an entire image is also used for training a “global” network. The age and gender classification results are obtained by combining both the outputs from both the “global” and the local networks. Our proposed model is tested on two face image benchmark datasets; competitive performance is obtained compared to the state-of-the-art methods.
Highlights
Age estimation and gender distinction from face images play important roles in many computer vision-based applications, such as visual surveillance, security control, and humancomputer interaction
Some image descriptors which are more powerful for image representation have been used in the area of age estimation and gender recognition tasks, such as local binary patterns (LBP) [5], shiftinvariant feature transform (SIFT) [6], Gabor filters [7], histogram of oriented gradient (HOG) [8], and biologically inspired features (BIF) [9]
In order to reduce the cost on convolutional neural networks (CNN) model training, a local deep neural network (LDNN) was proposed [20] for gender recognition; the LDNN model can achieve state-ofthe-art performance while the training cost is considerably reduced
Summary
Age estimation and gender distinction from face images play important roles in many computer vision-based applications, such as visual surveillance, security control, and humancomputer interaction. Many methods have been proposed to tackle the age and gender classification task. When the resolutions of images increase, directly using intensity values dramatically increases the scales of image features as well. Some image descriptors which are more powerful for image representation have been used in the area of age estimation and gender recognition tasks, such as local binary patterns (LBP) [5], shiftinvariant feature transform (SIFT) [6], Gabor filters [7], histogram of oriented gradient (HOG) [8], and biologically inspired features (BIF) [9]. The tasks of age estimation and gender classification have been widely investigated over the last decades, the results obtained are still far away from real applications [10, 11]
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