کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
532182 | 869918 | 2013 | 16 صفحه PDF | دانلود رایگان |
• A novel biometric image feature extraction method D-HLDO is proposed.
• D-HLDO means discriminative histogram of local dominant orientation.
• LM-NNDA is used to obtain the low-dimensional and discriminative feature.
• Experimental results demonstrate the effectiveness of the proposed D-HLDO method.
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.
Journal: Pattern Recognition - Volume 46, Issue 10, October 2013, Pages 2724–2739