Soft Label With Channel Encoding for Dependent Facial Image Classification

Bing Fei Wu, Yi Chiao Wu*, Li Wen Chiu, Hsuan Po Liu


研究成果: Article同行評審


In classification tasks, training labels are usually specified as one-hot targets which represent each class equally and exclusively. However, this labeling rule is not suitable in some situations. For the dependent classes, one-hot targets are not capable to represent the relation among them. The existing label smoothing methods just split the target response into neighboring classes, but it is only applied for ordinal classification, but not for the dependent but non-ordered classes. In this paper, we propose a novel labeling rule that decomposes the one-hot target into several bases to reflect relationships among classes while maintaining a balanced target space, which adopts channel encoding from communication systems, in particular, Bose–Chaudhuri–Hocquenghem (BCH) encoding. Besides, BCH encoder has an error-correcting mechanism that is expected to lift the accuracy. In theory, training with BCH targets ensures improved classification performance given the original accuracy is not less than 50%. To verify the proposed method on dependent classification, we conduct experiments with two facial tasks: age recognition and face anti-spoofing. The former is an ordinal classification task, and the latter is also regarded as a specific dependent classification problem due to the varying attack types being classified as one class finally and real for the other. Experimental results show that the proposed method improves accuracy by 6.33% on age recognition and reduces HTER by 3.63% for face anti-spoofing. In addition, as BCH targets divide the original response into a higher dimensional space, that is, the model is made to be heeded on learning the delicate sub-features. Hence, BCH targets also enhance model generalizability, thus guaranteeing improved performance on cross-domain evaluations. We further perform an assessment on the PACS dataset for evaluating domain generalizability. The results show that the domain generalizability is enhanced by increasing average accuracy by over 2% training with BCH targets.

頁(從 - 到)10661-10672
期刊IEEE Access
出版狀態Published - 2022


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