Abstract: To build flexible systems that work in a variety of lighting conditions and run on mobile phones or handheld PCs, robust and efficient face detection algorithms are required. Appearance-based methods are mainly employed to achieve high detection accuracy. They solve a two-class problem by using a probabilistic framework or finding a discriminant function from a large set of training examples. To solve this problem, it is necessary to find more distinctive features, which can capture the structural similarities within the face class. In this paper, I’m propose a new feature, called joint Haar-like feature, for detecting faces in images. This is based on co-occurrence of multiple Haarlike features. Feature co-occurrence, which captures the characteristics of human faces, makes it possible to construct a more powerful classifier. The joint Haar-like feature can be calculated very fast independently of image resolution and has robustness against addition of noise and change in illumination.
Keywords: algorithms detection, probabilistic framework, finding a discriminant function, Haar-like feature.
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DOI:
10.17148/IJARCCE.2021.10823
