FACE RECOGNITION BLOCK BASED STATICAL DCT AND TEXTURE | ||||
| Journal of the ACS Advances in Computer Science | ||||
| Article 5, Volume 10, Issue 1, 2019, Page 79-99 PDF (581.27 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/asc.2020.157427 | ||||
 View on SCiNiTO | ||||
| Abstract | ||||
| In this study, a feature extraction methodology proposed for face recognition. The proposed methodology uses combined block DCT and texture feature. That is, the feature extraction used is combination of frequency and spatial domains. The frequency domain feature is statistical block based discrete cosine transformation of a small number of low frequency coefficients. The texture part of the feature vector used is based on cooccurrence matrix of the images higher frequencies. The classification vehicle used in the study is the micro-classifier network. The micro-classifier network is a deterministic four layers’ neural network, the four layers are: input, micro-classifier, counter, and output. The network provides confidence factor, as well as proper generalization. Also, the network allows incremental learning, and more natural. The overall proposed face recognition methodology was tested using the standard ORL data set. The experimental results of the methodology showed comparable performance. | ||||
| Keywords | ||||
| Texture features; Classifier, Feature extraction; Image processing; Discrete Cosine transform; DCT | ||||
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