Machine learning has been gaining momentum over last decades: self-driving cars, efficient web search, speech and image recognition. The successful results gradually propagate into our daily live. Machine learning is a class of artificial intelligence methods, which allows the computer to operate in a self-learning mode, without being explicitly programmed. It is a very interesting and complex topic, which could drive the future of technology. Face detection is an important step in face recognition and emotion recognition, which is one of the more representative and classic application in computer vision. Face is one of the physiological bio-metrics based on stable features. Face detection by computer systems has become a major field of interest. Face detection algorithms are used in wide range of applications, such as security control, video retrieving, biometric signal processing, human computer interface, emotion detection, face recognition and image database management. Face detection is a challenging mission because faces in the images are all uncontrolled. E.g. illumination condition, vary pose, different facial expressions.
Kumar, P. & Bhardwaj, H (2022). Face Detection By Open CV. Afribary. Retrieved from https://tracking.afribary.com/works/face-detection-by-open-cv
Kumar, Prashant, and Harshit Bhardwaj "Face Detection By Open CV" Afribary. Afribary, 07 Mar. 2022, https://tracking.afribary.com/works/face-detection-by-open-cv. Accessed 24 Nov. 2024.
Kumar, Prashant, and Harshit Bhardwaj . "Face Detection By Open CV". Afribary, Afribary, 07 Mar. 2022. Web. 24 Nov. 2024. < https://tracking.afribary.com/works/face-detection-by-open-cv >.
Kumar, Prashant and Bhardwaj, Harshit . "Face Detection By Open CV" Afribary (2022). Accessed November 24, 2024. https://tracking.afribary.com/works/face-detection-by-open-cv