Using Facial Asymmetry Properties and Hidden Markov Models for Biometric Authentication in Security Systems

Abstract

Abstract. This work concerns the use of biometric features, resulting from the look of a face, for the authentication purposes. For this we propose several different methods of selection and feature analysis during face recognition. The description contains mainly the possibility of the analysis and in later stages also identity verification based on asymmetric facial features. The new authentication method has been introduced on the basis of designated characteristic points of face. The method includes propositions of our own algorithms of face detection, as well as face features extraction methods and their specific coding in the form of observation vectors and recognition using Hidden Markov Models.

Automatic Classification of Fruit Defects based on Co-Occurrence Matrix and Neural Networks

“…Several methods based on more specific image acquisition are reported in [7], [8], [9], [10], [11], [12], [13]. Wen and Tao developed a nearinfrared vision system for automatic apple defect inspection, see [14], While some recent advances in feature extraction for images and biometrics are reported in [15], [16], [17], [18], [19]. Zion et al introduced a computerised method to detect the bruises of Jonathan, Golden Delicious, and Hermon apples from magnetic resonance images by threshold technique.…”