Multi‐stage age estimation using two level fusions of handcrafted and learned features on facial images. Issue 2 (21st November 2018)

• Record Type: Journal Article
• Authors: Taheri, Shahram; Toygar, Önsen
• Is Part Of: IET biometrics. Volume 8:Issue 2(2019)
• Page Start: 124
• Page End: 133
• Publication Date: 2018-11-21
• Subjects: face recognition -- neural nets -- ageing -- learning (artificial intelligence) -- biometrics (access control) -- image classification -- feature extraction; generic feature extractor -- age‐related handcrafted features -- decision‐level fusion -- estimated ages -- feature‐level fusion -- different handcrafted local feature descriptors -- skin -- facial component -- score‐level fusion -- different feature layers -- in‐the‐wild age databases -- multistage age estimation -- level fusions -- handcrafted learned features -- facial images -- facial variations -- misalignment -- facial expressions -- age estimation system -- multistage features; Biometric identification -- Periodicals; 570.15195
• DOI: 10.1049/iet-bmt.2018.5141
• Languages: English
• ISSNs: 2047-4938

Abstract

Abstract : Age estimation from facial images is an important application of biometrics. In contrast to other facial variations like occlusions, illumination, misalignment and facial expressions, ageing variation is affected by human genes, environment, lifestyle and health which make age estimation a challenging task. In this study, the authors propose a new age estimation system which exploits multi‐stage features from a generic feature extractor, a trained convolutional neural network (CNN), and precisely combined these features with a selection of age‐related handcrafted features. This method utilises a decision‐level fusion of estimated ages by two different approaches; the first one uses feature‐level fusion of different handcrafted local feature descriptors for wrinkle, skin and facial component, while the second one uses score‐level fusion of different feature layers of a CNN for its age estimation. Experiments on the publicly available MORPH‐Album‐2 and FG‐NET databases prove the effectiveness of the novel method. Moreover, an additional experimental study on AgeDB database demonstrates that the proposed method is comparable with the best state‐of‐the‐art system for age estimation using in‐the‐wild age databases.