Activity related authentication using prehension biometrics. Issue 5 (May 2015)

• Record Type: Journal Article
• Authors: Drosou, Anastasios; Ioannidis, Dimosthenis; Tzovaras, Dimitrios; Moustakas, Konstantinos; Petrou, Maria
• Is Part Of: Pattern recognition. Volume 48:Issue 5(2015:May)
• Page Start: 1743
• Page End: 1759
• Publication Date: 2015-05
• Subjects: Biometrics -- Behavioural biometrics -- Biometric recognition -- Biometric authentication -- Prehension biometrics -- Activity Curves -- Activity surfaces -- Activity related biometrics -- Spherical harmonics -- DTW; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2014.12.008
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: This paper presents an extensive study on prehension-based dynamic features and their use for biometric purposes. The term prehension describes the combined movement of reaching, grasping and manipulating objects. The motivation behind the proposed study derives from both previous works related to the human physiology and human motion, as well as from the intuitive assumption that different body types and different characters would produce distinguishable, and thus valuable for biometric verification, activity-related traits. A novel approach for analyzing such movements is presented herein, based on the generation of an activity related manifold, the Activity hyper-Surface . The authentication capacity of the extracted features on the activity hyper-surface is evaluated in terms of their relative entropy and their mutual information within a complete framework targeting user verification. Experimental results on two datasets of 29 real subjects each and a third one of 100 virtual subjects show that the introduced concept constitutes a promising approach in the field of biometric recognition. Abstract : Highlights: We justify the validity of a prehension movement as biometric trait. We propose Activity hyper-Surfaces as descriptors for the arm movements. We analyze and combine Activity Curves of finger movements for completeness. Relative Entropy & Mutual Information based algorithm for dimensionality reduction. We evaluate our system on two real medium sized datasets and a large synthetic one.