Face Detection using Skin Segmentation as Pre-Filter

Shobana, L.; Yekkala, Anil Kr.; Eajaz, Sameen

doi:10.1142/9789812772381_0007

Cited by 1 publication

(2 citation statements)

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“…Skin-color has been shown to be useful for face detection [8,9]. There are some approaches in the literature that try to combine the VJ face detector with skin-color detector, which are mainly based on pre-filtering [9,10,11]. Instead of making an exhaustive search on the whole image, VJ algorithm is applied around probable face regions, where skin-color pixels are highly populated.…”

Section: Previous Workmentioning

confidence: 99%

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Combining Haar Feature and skin color based classifiers for face detection

Erdem

Ulukaya

Karaali

et al. 2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper presents a hybrid method for face detection in color images. The well known Haar feature-based face detector developed by Viola and Jones (VJ), that has been designed for gray-scale images is combined with a skin-color filter, which provides complementary information in color images. The image is first passed through a Haar-Feature based face detector, which is adjusted such that it is operating at a point on its ROC curve that has a low number of missed faces but a high number of false detections. Then, using the proposed skin color post-filtering method many of these false detections can be eliminated easily. We also use a color compensation algorithm to reduce the effects of lighting. Our experimental results on the Bao color face database show that the proposed method is superior to the original VJ algorithm and also to other skin color based pre-filtering methods in the literature in terms of precision.

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Section: Previous Workmentioning

confidence: 99%

“…In order to reduce the effects of illumination, we also use a color compensation method before the skin-color detection step to improve the effectiveness of skin color detection, which was not present in previous pre-filtering based approaches [9,10].…”

Section: Contributions and Outline Of The Papermentioning

confidence: 99%