Abstract
Age estimation from face images has attracted much attention due to its favorable and many real-world applications such as video surveillance and social networking. However, most existing studies usually learn a single kind of age feature and ignore other appearance features such as gender and race, which have a great influence on the age pattern. In this paper, we proposed a compact multifeature learning and fusion method for age estimation. Specifically, we first used three subnetworks to learn gender, race, and age information. Then, we fused these complementary features to further form more robust features for age estimation. Finally, we engineered a regression-ranking age-feature estimator to convert the fusion features into the exact age numbers. Experimental results on three benchmark databases demonstrated the effectiveness and efficiency of the proposed method on facial age estimation in comparison to previous state-of-the-art methods. Moreover, compared with previous state-of-the-art methods, our model was more compact with only a 20 MB memory overhead and is suitable for deployment on mobile or embedded devices for age estimation.
Highlights
Age estimation is performed to identify a human’s age from face images, which has broad application scenarios in public areas
Traditional age-feature-learning methods are based on hand-crafted features, such as the Local Binary Pattern (LBP) [3], the Histogram of Oriented Gradients (HOG) [4], and Biologically Inspired Features (BIF) [5]
The experimental results clearly showed that our proposed method could achieve a higher accuracy for age estimation than most state-of-the-art age-estimation methods
Summary
Age estimation is performed to identify a human’s age from face images, which has broad application scenarios in public areas. Traditional age-feature-learning methods are based on hand-crafted features, such as the Local Binary Pattern (LBP) [3], the Histogram of Oriented Gradients (HOG) [4], and Biologically Inspired Features (BIF) [5]. These hand-crafted-based features require strong prior knowledge to engineer them by hand [6]. To address this limitation, deep-learning-based techniques have been proposed and have shown great success in age-feature learning in recent years [7,8]. The recent age-feature-learning studies mainly focus on deep-learning networks such as VGG-16 [9], AlexNet [10], and MobileNet [11]
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