Automated face recognition should accurately detect the subject’s face with lesser computational complexity (computational time and memory). The eyes, mouth, and nose served as primary reference points to identify users’ faces in the scene [45]. Though different face detection methods were reported in recent years [14, 45], the Viola and Jones face detection method is mostly referred to in earlier works for real-time facial emotion recognition than other face detection methods [4, 16, 46]. Viola and Jones have utilized Haar-like features in the detection of the face, eyes, nose, and mouth [46]. Haar-like features were used to compute pixel contrast (white and black) between adjacent rectangular groups using lines, edges, and center-surround features instead of using the image’s original pixel values in face detection. Thereby, this method required lesser computation time and memory for face detection than other methods [46]. Recently, AdaBoost cascade classifier employed in Haar-like features to detect human face in the scene efficiently [48]. OpenCV library is utilized in the present work to capture the image sequences from the webcam. The captured image sequences are converted from color image to greyscale image before implementing the face detection method. Consequently, the subject’s face and eyes in the video were detected using Haar-like features. The face detection using this proposed method is faster than other methods in the literature (computation time: 0.067 sec) [47]. Finally, the algorithm proposed in our earlier work [47] formulates an ellipse around the face, positioned “+” markers on both eyes of the subject’s to ease the process of computer-generated marker placement [48, 49]. A sample subject after face and eye detection is shown in Fig 3(a) and 3(b), respectively.

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