Identification of human faces through texture-based feature recognition and neural network technology

Combining rectangular and triangular image regions to perform real-time face detection

“…[17]), or nonlinear decision surfaces (e.g., [14]). Regarding the key feature extraction stage, texture-based features are often used, through second-order statistical moments (e.g., Augusteijn and Skufca [1]). Starting from RGB images, Crowley and Berard [5] computed the Red and Green components histograms to obtain the probability of a particular RGB vector, given that the pixel observes skin.…”

Improved Facial-Feature Detection for AVSP via Unsupervised Clustering and Discriminant Analysis

“…Techniques using simple grayscale texture measures have been investigated by researchers. Augusteijn and Skujca [10] were able to gain effective segmentation results by computing second-order statistical features on 16 × 16 grayscale subimages. Using a neural network, they were able to train the classifier using face and nonface textures, with good results reported.…”

User, Gesture and Robot Behaviour Adaptation for Human-Robot Interaction

“…Visual detecting of face has been studied extensively over the last decade. Face detection researchers summarized the face detection work into four categories: template matching approaches [Sakai, 1996] [Miao, 1999] [Augusteijn, 1993] [Yuille, 1992] [Tsukamoto, 1994]; feature invariant approaches [Sirohey, 1993]; appearance-based approaches [Turk, 1991] and knowledge-based approaches [Yang, 1994] [Yang, 2002] [ Kotropoulous, 1997]. Many researches also used skin color as a feature and leading remarkably face tracking as long as the lighting conditions do not varies too much [Dai, 1996], [Crowley, 1997] , [Hasanuzzaman 2004b].…”