Human Face Detection Using Silhouettes

Samal, Ashok; Iyengar, Prasana A.

doi:10.1142/s0218001495000353

Cited by 23 publications

(8 citation statements)

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“…Samal and Iyengar [164] proposed a PCA face detection scheme based on face silhouettes. Instead of eigenfaces, they generate eigensilhouettes and combine this with standard image processing techniques (edge detection, thinning, thresholding) and the generalized Hough transform.…”

Section: Linear Subspace Methodsmentioning

confidence: 99%

Face Detection: A Survey

Hjelmås

Low

2001

Computer Vision and Image Understanding

960

426

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Section: Linear Subspace Methodsmentioning

confidence: 99%

Face Detection: A Survey

Hjelmås

Low

2001

Computer Vision and Image Understanding

960

426

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“…Such features, however, can be easily corrupted by noise and illumination changes. Other researchers [26], [27], [28] use standard face patterns as templates and evaluate the correlation between a new image and the pattern images for a number of different features (eyes, nose, mouth, face contour). Limitations in scale, pose and shape variations can be overcome at the expense of increased computational burden.…”

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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“…Silhouettes have also been used as templates for face localization [134]. A set of basis face silhouettes is obtained using principal component analysis (PCA) on face examples in which the silhouette is represented by an array of bits.…”

Section: Predefined Templatesmentioning

confidence: 99%

Detecting faces in images: a survey

Yang

Kriegman

Ahuja

2002

IEEE Trans. Pattern Anal. Machine Intell.

2,614

117

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ÐImages containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless of its three-dimensional position, orientation, and the lighting conditions. Such a problem is challenging because faces are nonrigid and have a high degree of variability in size, shape, color, and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.

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Human Face Detection Using Silhouettes

Cited by 23 publications

References 0 publications

Face Detection: A Survey

Face Detection: A Survey

A monocular system for person tracking: Implementation and testing

Detecting faces in images: a survey

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