Improving the Efficiency of Image Captured via PGC and Stereo Vision Using Novel VJ Algorithm in Comparison with Local Binary Pattern Histogram Algorithm

The study compares the local binary pattern histogram method to a PGC and stereo vision picture acquired utilising innovative violas jones algorithm. An automated face identification and recognition system was tested utilising the innovative violas jones algorithm and Local Binary Pattern Histogram (LBPH) Algorithm using 20 samples at various periods. The G power with 0.8 pre-testing power, 0.05 alpha, and 0.95 confidence interval is suggested for SPSS accuracy prediction. The model is tested and trained on 40 kaggle (kaggle.com) samples. Automatic face identification and recognition system performance was assessed using the innovative violas jones method and Local Binary Pattern Histogram method with 20 samples at various periods. After statistical analysis, the violas jones algorithm achieved 93% accuracy, while the local binary pattern histogram algorithm achieved 84% accuracy using the default setting. The independent sample t-test significance was =0.001 (<0.05).