Abstract
Face gender recognition has many useful applications in human–robot interactions as it can improve the overall user experience. Support vector machines (SVM) and convolutional neural networks (CNNs) have been used successfully in this domain. Researchers have shown an increased interest in comparing and combining different feature extraction paradigms, including deep-learned features, hand-crafted features, and the fusion of both features. Related research in face gender recognition has been mostly restricted to limited comparisons of the deep-learned and fused features with the CNN model or only deep-learned features with the CNN and SVM models. In this work, we perform a comprehensive comparative study to analyze the classification performance of two widely used learning models (i.e., CNN and SVM), when they are combined with seven features that include hand-crafted, deep-learned, and fused features. The experiments were performed using two challenging unconstrained datasets, namely, Adience and Labeled Faces in the Wild. Further, we used T-tests to assess the statistical significance of the differences in performances with respect to the accuracy, f-score, and area under the curve. Our results proved that SVMs showed best performance with fused features, whereas CNN showed the best performance with deep-learned features. CNN outperformed SVM significantly at p < 0.05.
Highlights
Gender recognition is vital in interconnected information societies; it has applications in many domains such as security surveillance, targeted advertising, and human–robot interactions
Our T-test shows that the result with the Adience dataset (p = 0.0002) is significant whereas the result with Labeled Faces in the Wild (LFW) (p = 0.093) is insignificant at p < 0.05. These results suggest that convolutional neural networks (CNNs) with deep-learned features is superior to Support vector machines (SVM) using any type of feature
Face gender recognition plays a key role in robot–human interactions since it allows robots to adapt their behavior based on the gender of the interacting user, which increases user acceptance and satisfaction
Summary
Gender recognition is vital in interconnected information societies; it has applications in many domains such as security surveillance, targeted advertising, and human–robot interactions. Face gender recognition is still considered a challenging and unsolved problem; researchers continue to seek a solution [15,18]. There are numerous reasons for considering face gender recognition an open research problem. Numerous reported successes in the literature are achieved with easy constrained datasets, such as facial recognition technology (FERET) [19,20,21,22] and UND [20]. These datasets contain the frontal face images that were captured under controlled conditions of facial expressions, illumination, and background
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