Projection based method for segmentation of human face and its evaluation

Baskan, Selin; Bulut, Mehmet; Atalay, Volkan

doi:10.1016/s0167-8655(02)00037-5

Cited by 76 publications

(34 citation statements)

References 12 publications

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“…Most eye detection methods exploit the observation that eye regions usually exhibit sharp changes in both luminance and chrominance, in contrast with the surrounding skin. Researchers have employed integral projection [22], morphological filters [23], edge map analysis [24], and non-skin color area detection to find potential eye locations in the facial image.…”

Section: B Eye Position Detectionmentioning

confidence: 99%

Robust Algorithm for Face Detection in Color Images

Tin¹

2012

IJMECS

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Abstract-Robust Algorithm is presented for frontal face detection in color images. Face detection is an important task in facial analysis systems in order to have a priori localized faces in a given image. Applications such as face tracking, facial expression recognition, gesture recognition, etc., for example, have a pre-requisite that a face is already located in the given image or the image sequence. Facial features such as eyes, nose and mouth are automatically detected based on properties of the associated image regions. On detecting a mouth, a nose and two eyes, a face verification step based on Eigen face theory is applied to a normalized search space in the image relative to the distance between the eye feature points. The experiments were carried out on test images taken from the internet and various other randomly selected sources. The algorithm has also been tested in practice with a webcam, giving (near) real-time performance and good extraction results.

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Section: B Eye Position Detectionmentioning

confidence: 99%

Robust Algorithm for Face Detection in Color Images

Tin¹

2012

IJMECS

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“…The eye detection is done in the face regions which have been already located [1,2,3,4]. Little research has been done, however, on the direct search for eyes in whole images.…”

Section: Introductionmentioning

confidence: 99%

Template Matching based Eye Detection in Facial Image

Bhoi¹,

Mohanty²

2010

IJCA

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Eye detection is a pre-requisite stage for many applications such as human-computer interfaces, iris recognition, driver drowsiness detection, security, and biology systems. In this paper, template based eye detection is described. The template is correlated with different regions of the face image. The region of face which gives maximum correlation with template refers to eye region. The method is simple and easy to implement. The effectiveness of the method is demonstrated in both the cases like open eye as well as closed eye through various simulation results.

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“…The projections can be applied on the first derivative of the image [3] or directly on the intensities values. Projection based methods [7,9] have been used particularly to find coarsely the position of the facial features. The projection method is being combined with other methods such as genetic algorithm [8] and gabor transformation [18] .…”

Section: Introductionmentioning

confidence: 99%

“…The projection method is being combined with other methods such as genetic algorithm [8] and gabor transformation [18] . The amount of work that gave qualitative results about the performance of features localization is limited as most of the previous studies [7,9,12,13] excluded the effects of beard or moustache.…”

Section: Introductionmentioning

confidence: 99%

Facial Features for Template Matching Based Face Recognition

Chai¹,

Rizon²,

San³

et al. 2009

American J. of Applied Sciences

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Problem statement: Template matching had been a conventional method for object detection especially facial features detection at the early stage of face recognition research. The appearance of moustache and beard had affected the performance of features detection and face recognition system since ages ago. Approach: The proposed algorithm aimed to reduce the effect of beard and moustache for facial features detection and introduce facial features based template matching as the classification method. An automated algorithm for face recognition system based on detected facial features, iris and mouth had been developed. First, the face region was located using skin color information. Next, the algorithm computed the costs for each pair of iris candidates from intensity valleys as references for iris selection. As for mouth detection, color space method was used to allocate lips region, image processing methods to eliminate unwanted noises and corner detection technique to refine the exact location of mouth. Finally, template matching was used to classify faces based on the extracted features. Results: The proposed method had shown a better features detection rate (iris = 93.06%, mouth = 95.83%) than conventional method. Template matching had achieved a recognition rate of 86.11% with acceptable processing time (0.36 sec). Conclusion: The results indicate that the elimination of moustache and beard has not affected the performance of facial features detection. The proposed features based template matching has significantly improved the processing time of this method in face recognition research.

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Projection based method for segmentation of human face and its evaluation

Cited by 76 publications

References 12 publications

Robust Algorithm for Face Detection in Color Images

Robust Algorithm for Face Detection in Color Images

Template Matching based Eye Detection in Facial Image

Facial Features for Template Matching Based Face Recognition

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