Detecting faces in images is the first step of any face application such as face recognition, face localization and face expression. The performance of face detection systems directly effect on correct operating of mentioned applications. Because faces are non-rigid and have high variation in scale, color, pose and lighting condition, designing an automatic system to overcome all mentioned problems is difficult. Machine learning has been shown that is one of the most successful tools to build high performance face detection systems. Due to huge number of studies in this area, studying all works in this field is tedious. The purpose of this study is to present categorize and evaluate some new face detection techniques using machine learning. The performance and the other evaluation parameters of these methods compare with each other in order to introduce significant techniques.
Kumar, P. & , V (2021). Viola-Jones Based Face Detection Algorithm. Afribary. Retrieved from https://tracking.afribary.com/works/viola-jones-based-face-detection-algorithm
Kumar, Prashant, and Viola-Jones "Viola-Jones Based Face Detection Algorithm" Afribary. Afribary, 22 Dec. 2021, https://tracking.afribary.com/works/viola-jones-based-face-detection-algorithm. Accessed 24 Nov. 2024.
Kumar, Prashant, and Viola-Jones . "Viola-Jones Based Face Detection Algorithm". Afribary, Afribary, 22 Dec. 2021. Web. 24 Nov. 2024. < https://tracking.afribary.com/works/viola-jones-based-face-detection-algorithm >.
Kumar, Prashant and , Viola-Jones . "Viola-Jones Based Face Detection Algorithm" Afribary (2021). Accessed November 24, 2024. https://tracking.afribary.com/works/viola-jones-based-face-detection-algorithm