Face Recognition Based Rank Reduction Svd Approach

Standard face recognition algorithms that use standard feature extraction techniques always suffer from image performance degradation. recently, singular value decomposition and low-rank matrix are applied in many applications, including pattern recognition and feature extraction. the main objective of this research is to design an efficient face recognition approach by combining many techniques to generate efficient recognition results. the implemented face recognition approach is concentrated on obtaining significant rank matrix via applying a singular value decomposition technique. measures of dispersion are used to indicate the distribution of data. according to the applied ranks, there is an adequate reasonable rank that is important to reach via the implemented procedure. interquartile range, mean absolute deviation, range, variance, and standard deviation are applied to select the appropriate rank. rank 24, 12, and 6 reached an excellent 100% recognition rate with data reduction up to 2 : 1, 4 : 1 and 8 : 1 respectively. in addition, properly selecting the adequate rank matrix is achieved based on the dispersion measures. obtained results on standard face databases verify the efficiency and effectiveness of the implemented approach.