Frontal-view face detection and facial feature extraction using color, shape and symmetry based cost functions

Saber, Eli; Tekalp, A. Murat

doi:10.1016/s0167-8655(98)00044-0

Cited by 178 publications

(94 citation statements)

References 22 publications

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“…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].…”

Section: Face Detectionmentioning

confidence: 99%

“…Yang and Ahuja [Yang, 1998] proposed a face detection method based on color, structure and geometry. Saber and Tekalp [Saber, 1998] presented a frontal view-face localization method based on color, shape and symmetry.…”

Section: Face Detectionmentioning

confidence: 99%

“…There are two different approaches for vision-based modelling of gesture: Model based approach and Appearance based approach. The Model based techniques are tried to create a 3D model of the user hand (parameters: Joint angles and palm position) [Rehg, 1994] or contour model of the hand [Shimada, 1996] [Lin, 2002] and use these for gesture recognition. The 3D models can be classified in two large groups: volumetric model and skeletal models.…”

Section: Gesture Recognition and Gesture Based Interfacementioning

confidence: 99%

See 2 more Smart Citations

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

Hasanuzzaman¹,

Ueno²

2012

The Future of Humanoid Robots - Research and Applications

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Section: Face Detectionmentioning

confidence: 99%

Section: Face Detectionmentioning

confidence: 99%

Section: Gesture Recognition and Gesture Based Interfacementioning

confidence: 99%

See 1 more Smart Citation

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

Hasanuzzaman¹,

Ueno²

2012

The Future of Humanoid Robots - Research and Applications

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“…The facial features used in such methods include the eyes, eyebrows, hair, nose and the mouth. Symmetry of the human face [22], as well as biometric information, i.e. distances between facial features [23] have also been exploited.…”

Section: Face Detection Based On Fusion Of Informationmentioning

confidence: 99%

A monocular system for person tracking: Implementation and testing

Stamou

Krinidis

Nikolaidis

et al. 2007

J Multimodal User Interfaces

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This paper presents a complete functional system capable of detecting people and tracking their motion in either live camera feed or pre-recorded video sequences. The system consists of two main modules, namely the detection and tracking modules. Automatic detection aims at locating human faces and is based on fusion of color and feature-based information. Thus, it is capable of handling faces in different orientations and poses (frontal, profile, intermediate). To avoid false detections, a number of decision criteria are employed. Tracking is performed using a variant of the well-known Kanade-Lucas-Tomasi tracker, while occlusion is handled through a re-detection stage. Manual intervention is allowed to assist both modules if required. In manual mode, the system can track any object of interest, so long as there are enough features to track. The system caters for calibrated cameras and can provide 3-D coordinates of any tracked object(s) of interest. It has been tested with very good results on a variety of video sequences, including a database of studio video sequences, for which 3-D ground truth data, originating from a 4-camera infrared tracking system, exist.

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“…Saber and Tekalp [Saber, 1998] presented a frontal view-face localization method based on color, shape and symmetry. Darrel et.…”

Section: Feature Invariant Approachesmentioning

confidence: 99%