This work aimed to find the most discriminative facial regions between the eyes and eyebrows for periocular biometric features in a partial face recognition system. We propose multiscale analysis methods combined with curvature-based methods. The goal of this combination was to capture the details of these features at finer scales and offer them in-depth characteristics using curvature. The eye and eyebrow images cropped from four face 2D image datasets were evaluated. The recognition performance was calculated using the nearest neighbor and support vector machine classifiers. Our proposed method successfully produced richer details in finer scales, yielding high recognition performance. The highest accuracy results were 76.04% and 98.61% for the limited dataset and 96.88% and 93.22% for the larger dataset for the eye and eyebrow images, respectively. Moreover, we compared the results between our proposed methods and other works, and we achieved similar high accuracy results using only eye and eyebrow images.

Abstract

Hand gestures, as a part of human body language, can be used for many purposes. By means of a hand gesture recognizer, we could communicate with machines using our hand gestures. A recognition system typically consists of preprocessing steps and a classifier. This paper presents an analysis of using edge detection and/or histogram equalization in the preprocessor by examining the overall performance of the hand gesture recognition system. Nearest neighbor classifier is used as a classifier in the recognition system. The system aims to classify the input images into one of six classes. Each class represents a different command to a machine. The hand gesture images are taken using a web camera under controlled condition and a uniform white background. The system performance is measured by using cross validation method. The experiment results show that using histogram equalization and edge detection as feature extractor lowered the average of accuracy.

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