In recent years face recognition has received substantial attention from both research communities and the market, but still remained very challenging in real applications. A lot of face recognition algorithms, along with their modifications, have been developed during the past decades. A number of typical algorithms are presented, being categorized into appearance based and model-based schemes. In this paper we will represent a new method for face detection called Minimum Distance Detection Approach (MDDA) . The obtained results clearly confirm the efficiency of the developed model as compared to the other methods in terms of Classification accuracy. It is also observed that the new method is a powerful feature selection tool which has indentified a subset of best discriminative features. Additionally, the proposed model has gained a great deal of efficiency in terms of CPU time owing to the parallel implementation. In this model we use a direct model for face detection without using unlabelled data. In this research we tarry to identify one sample from a group of unknown samples using a sequence of processes . The results show that this method is very effective when we use a large a sample of unlabelled data to detect on sample.


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