Commenced in January 2007
Paper Count: 30172
Finding Sparse Features in Face Detection Using Genetic Algorithms
Abstract:Although Face detection is not a recent activity in the field of image processing, it is still an open area for research. The greatest step in this field is the work reported by Viola and its recent analogous is Huang et al. Both of them use similar features and also similar training process. The former is just for detecting upright faces, but the latter can detect multi-view faces in still grayscale images using new features called 'sparse feature'. Finding these features is very time consuming and inefficient by proposed methods. Here, we propose a new approach for finding sparse features using a genetic algorithm system. This method requires less computational cost and gets more effective features in learning process for face detection that causes more accuracy.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1331339Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1263
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