Combined training strategy for low‐resolution face recognition with limited application‐specific data. Issue 10 (17th July 2019)

• Record Type: Journal Article
• Authors: Zeng, Dan; Spreeuwers, Luuk; Veldhuis, Raymond; Zhao, Qijun
• Is Part Of: IET image processing. Volume 13:Issue 10(2019)
• Page Start: 1790
• Page End: 1796
• Publication Date: 2019-07-17
• Subjects: Bayes methods -- feature extraction -- face recognition -- biometrics (access control) -- image resolution -- learning (artificial intelligence) -- convolutional neural nets -- neural net architecture; combined training strategy -- low‐resolution face recognition -- limited application‐specific data -- convolutional neural network -- CNN classifier architecture -- feature extractors -- heterogeneous joint Bayesian classifier -- HJB classifier -- SCface -- COX; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5732
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Application‐specific data for certain biometric applications are often not sufficiently available. The authors present a solution for face recognition with limited application‐specific data. Existing methods often use a classifier with convolutional neural networks (CNNs) as feature extractors. The CNNs are trained with massive general (i.e. not application specific) data and the classifier is trained with application‐specific data. Alternatively, the authors propose a combined training strategy to train the classifier on a balanced mixture of general and application‐specific data, such that the recognition performance is maximised. The proposed method largely alleviates the needs for application‐specific data. To prove its effectiveness, they apply the proposed method to low‐resolution face recognition. Specifically, they use the heterogeneous joint Bayesian (HJB) classifier that is capable of comparing features from the same modality but with different characteristics. To further boost performance, the authors augment the training data by pre‐processing it to resemble application‐specific data. They conducted extensive experiments on challenging datasets, namely, SCface and COX. The results show that the proposed method improves the true match rate on SCface at a false match rate of 10% by ∼11% and the true match rate on COX at a false match rate of 1% by ∼12%.