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|Title: ||A novel comparative deep learning framework for facial age estimation|
Abousaleh, Fatma S.;Lim, Tekoing;Cheng, Wen-Huang;Yu, Neng-Hao;Hossain, M. Anwar;Alhamid, Mohammed F.
|Keywords: ||Deep learning;Facial age estimation;Region convolutional neural network;Comparative framework|
|Issue Date: ||2017-07-12 14:14:02 (UTC+8)|
|Abstract: ||Developing automatic facial age estimation algorithms that are comparable or even superior to the human ability in age estimation becomes an attractive yet challenging topic emerging in recent years. The conventional methods estimate one person’s age directly from the given facial image. In contrast, motivated by human cognitive processes, we proposed a comparative deep learning framework, called Comparative Region Convolutional Neural Network (CRCNN), by first comparing the input face with reference faces of known age to generate a set of hints (comparative relations, i.e., the input face is younger or older than each reference). Then, an estimation stage aggregates all the hints to estimate the person’s age. Our approach has several advantages: first, the age estimation task is split into several comparative stages, which is simpler than directly computing the person’s age; secondly, in addition to the input face itself, side information (comparative relations) can be explicitly involved to benefit the estimation task; finally, few incorrect comparisons will not influence much the accuracy of the result, making this approach more robust than the conventional approach. To the best of our knowledge, the proposed approach is the first comparative deep learning framework for facial age estimation. Furthermore, we proposed to incorporate the Method of Auxiliary Coordinates (MAC) for training, which reduces the ill-conditioning problem of the deep network and affords an efficient and distributed optimization. In comparison to the best results from the state-of-the-art methods, the CRCNN showed a significant outperformance on all the benchmarks, with a relative improvement of 13.24% (on FG-NET), 23.20% (on MORPH), and 4.74% (IoG).|
|Relation: ||EURASIP Journal on Image and Video Processing, 2016(1), 47|
|Data Type: ||article|
|DOI 連結: ||http://dx.doi.org/10.1186/s13640-016-0151-4|
|Appears in Collections:||[資訊科學系] 期刊論文|
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