Finding faces in cluttered scenes using random labeled graph matching

Leung, Thomas; Burl, Michael C.; Perona, Pietro

doi:10.1109/iccv.1995.466878

Cited by 218 publications

(107 citation statements)

References 12 publications

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“…Shape models, in particular deformable shape models such as deformable template matching [15] and graph matching [4], have been used for face localization, i.e., finding accurate facial shape and parts. A good example is the active shape model (ASM) [1] where a Bayesian approach using a mixture of Gaussians is adopted.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Shape Modeling for Automatic Face Localization

Liu

Shum

Zhang

2002

Computer Vision — ECCV 2002

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Abstract. Many approaches have been proposed to locate faces in an image. There are, however, two problems in previous facial shape models using feature points. First, the dimension of the solution space is too big since a large number of key points are needed to model a face. Second, the local features associated with the key points are assumed to be independent. Therefore, previous approaches require good initialization (which is often done manually), and may generate inaccurate localization. To automatically locate faces, we propose a novel hierarchical shape model (HSM) or multi-resolution shape models corresponding to a Gaussian pyramid of the face image. The coarsest shape model can be quickly located in the lowest resolution image. The located coarse model is then used to guide the search for a finer face model in the higher resolution image. Moreover, we devise a Global and Local (GL) distribution to learn the likelihood of the joint distribution of facial features. A novel hierarchical data-driven Markov chain Monte Carlo (HDDMCMC) approach is proposed to achieve the global optimum of face localization. Experimental results demonstrate that our algorithm produces accurate localization results quickly, bypassing the need for good initialization.

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Section: Introductionmentioning

confidence: 99%

Hierarchical Shape Modeling for Automatic Face Localization

Liu

Shum

Zhang

2002

Computer Vision — ECCV 2002

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“…This is the knowledge based method and it is used to detect the face by using the structural nature of the face [4]. Feature based method uses the facial features [5][6], skin colour [7][8] and it is combined with the multiple features [9] of the face for better accuracy and detection speed, the accuracy is sacrificed. Template matching method was employed.…”

Section: IImentioning

confidence: 99%

Real Time Face Detection Using Matlab

Fathema¹,

Lakshmi²,

Ravali³

et al. 2018

IJERT

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Abstract---This electronic document is about face detection. In computer literature face detection has been one of the most studied topics. Given an arbitrary image, the goal of this project is to determine whether or not there are any faces in the image and detection of eyes and upperbody. While this appears to be a trivial task for human beings, it is very challenging task for computers. The difficulty associated with face detection can be attributed to many variations in scale, location, view point, illumination, occlusions, etc. Although there have been hundreds of reports reported approaches for face detection, if one were asked to name a single face detection algorithm that has most impact in recent decades, it will most likely be the Viola and Jones face detection, which is capable of processing images extremely rapidly and achieve high detection rates. In thiswe are going to study and understand the ViolaJones algorithm by implementing the detection frame work and based on that implementation, conduct experiment to improve the performance.

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“…By grouping the facial attributes based on their known geometrical relationships, faces are detected [48,113]. A drawback of this approach is the detection of facial attributes is not reliable [48], which leads to systems that are not robust against varying facial expressions and presence of other devices. This approach is better suited for facial expression recognition as opposed to face detection.…”

Section: Introductionmentioning

confidence: 99%