Enhancing Face Detection and Recognition through Machine Learning Algorithm

Face recognition is a rapidly growing field with multiple applications. Numerous face recognition algorithms have been developed over the years. In this research, use a Histogram of Oriented Gradients (HOG) based face detector for precise face detection, which surpasses other machine learning techniques, such as Haar Cascade. The suggested approach uses HOG for feature extraction and Contrast Limited Adaptive Histogram Equalization (CLAHE), two generally used pre-processing methods, throughout the recognition process. The test picture and the training images are both used to extract HOG features. Lastly, this system employs Support Vector Machine (SVM) for classification, as it effectively classifies the HOG features. Pre-processing techniques are utilized to equalize illumination, reduce noise, and enhance contrast. The study's findings show the dependability and effectiveness of this approach in achieving superior face recognition performance.