A Genetic Algorithm-Based Feature Selection for Kinship Verification

• Published in: IEEE signal processing letters Vol. 22; no. 12; pp. 2459 - 2463
• Main Authors: Alirezazadeh, Pendar, Fathi, Abdolhossein, Abdali-Mohammadi, Fardin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Biometric; Biometrics; Biometrics (access control); Face recognition; Feature extraction; feature selection; genetic algorithm; Genetic algorithms; Genetics; global and local features; kinship verification; Learning; Signal processing
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2015.2490805

One of the new challenges of biometric systems based on face analysis is kinship verification. Little efforts have been done in spite of the importance and functionality of this subject. Most of existing methods have been trying to exploit and represent techniques based on metric learning to increase verification rate, paying no attention to the effect of the features extracted from the faces. Despite the previous methods exploiting simple local features, we have focused on the combination and selection of effective features in this paper. To this end, local and global features were combined to describe the face images in a better way. The effective and discriminative features were selected using the kinship genetic algorithm and then fulfilled kinship verification. The proposed method is tested and analysed on the standard and big datasets KinFaceW-I and KinFaceW-II, and verification rates of 81.3% and 86.15% were obtained respectively.