Discriminative histograms of local dominant orientation (D-HLDO) for biometric image feature extraction

作者：Qian Jianjun; Yang Jian^*; Gao Guangwei

来源：Pattern Recognition, 2013, 46(10): 2724-2739.

DOI：10.1016/j.patcog.2013.03.005

• Feature extraction
• Principal component analysis (PCA)
• Biometrics
• Image representation
• Linear discriminant analysis (LDA)

This paper presents a simple and robust method, namely discriminative histograms of local dominant orientation (D-HLDO), for biometric image feature extraction. In D-HLDO, the local dominant orientation map and the corresponding relative energy map are obtained by applying the singular value decomposition (SVD) to the collected gradient vectors over a local patch. The dominant orientation map and the relative energy map are then used to construct the concatenated histogram features. Local mean based nearest neighbor discriminant analysis (LM-NNDA) is finally employed to reduce the redundancy information and get the low-dimensional and discriminative features. The proposed method is applied to face, finger-knuckle-print and Palm biometrics and is examined using the AR, CMU PIE and FRGCv2.0 face image databases, the PolyU Palmprint database, and the PolyU Finger-Knuckle-Print database. Experimental results demonstrate the effectiveness of the proposed D-HLDO method.

• 出版日期2013-10
• 单位南京大学