Finding faces in color images using wavelet transform

Karlekar, Jayashree; Desai, U.B.

doi:10.1109/iciap.1999.797744

Cited by 11 publications

(3 citation statements)

References 11 publications

(5 reference statements)

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“…The conversion also effectively suppresses the background of other colors and allows the detection of small faces in a natural environment. Other color models applied to face detection include HSV [48,60,81,216], YES [160], YCrCb [2,48,84,130,191,200], YUV [1,123], CIE-xyz [15], L * a * b * [13,109], L * u * v * [63], CSN (a modified rg representation) [90,91] and UCS/Farnsworth (a perceptually uniform color system was proposed by Farnsworth [210]) [208].…”

Section: Colormentioning

confidence: 99%

Face Detection: A Survey

Hjelmås

Low

2001

Computer Vision and Image Understanding

953

426

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Section: Colormentioning

confidence: 99%

Face Detection: A Survey

Hjelmås

Low

2001

Computer Vision and Image Understanding

953

426

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“…Further they have used mask based processing to identify face regions, from the skin pixels identified. Karlekar and Desai (1999) and Phung et al (2001) used MLP in CbCr space for skin classification. The MLP is trained from 200 images using Levenberg-Marquardt algorithm for faster convergence.…”

Section: Review Of Skin Color Models Using Neural Networkmentioning

confidence: 99%

Skin Color Detection Model Using Neural Networks and its Performance Evaluation

Bhoyar¹,

Kakde²

2010

Journal of Computer Science

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Problem statement: Skin color detection is used as a preliminary step in numerous computer vision applications like face detection, nudity recognition, hand gesture detection and person identification. In this study we present a pixel based skin color classification approach, for detecting skin pixels and non skin pixels in color images, using a novel neural network symmetric classifier. The neural classifiers used in the literature either uses a symmetric model with single neuron in the output layer or uses two separate neural networks (asymmetric model) for each of the skin and non-skin classes. The novelty of our approach is that it has two output layer neurons; one each for skin and non-skin class, instead of using two separate classifiers. Thus by using a single neural network classifier we have improved the separability between these two classes, eliminating additional time complexity that is needed in asymmetric classifier. Approach: Skin samples from web images of people from different ethnic groups were collected and used for training. Ground truth skin segmented images were obtained by using semiautomatic skin segmentation tool developed by the authors. The ground truth database of skin segmented images, thus obtained was used to evaluate the performance of our NN based classifier. Results: With proper selection of optimum classification threshold that varies from image to image the classifier gave the detection rate of more than 90% with 7% false positives on an average, Conclusion/Recommendations: It is observed that the neural network is capable of detecting skin in complex lighting and background environments. The classifier has the ability to classify the skin pixels belonging to people from different ethnic groups even when they are present simultaneously in an image. The proper choice of optimum classification threshold that varies from image to image is an issue here. Automatic computation of this optimum threshold for each image is desired in practical skin detection applications. This issue can be taken up as a future study, which will enable us to perform fully automatic skin segmentation with reduced false positives

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“…Some researches use the wavelet transform to detect face region. In Karlekar's 14 algorithm, chrominance (Cb and Cr) subimages are obtained from four-level of wavelet transform to check for candidate face pixels. Candidate face pixels are detected from the coarsest-level lowpass chrominance subimage and then, the binary mask image is applied to lowpass subimages after classification.…”

Section: Related Work With Wavelet-based Template Matchingmentioning

confidence: 99%

Robust Face Detection and Tracking for Real-Life Applications

Byun

2003

Int. J. Patt. Recogn. Artif. Intell.

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In this paper, we propose a new face detection and tracking algorithm for real-life telecommunication applications, such as video conferencing, cellular phone and PDA. We combine template-based face detection and tracking method with color information to track a face regardless of various lighting conditions and complex backgrounds as well as the race. Based on our experiments, we generate robust face templates from wavelet-transformed lowpass and two highpass subimages at the second level low-resolution. However, since template matching is generally sensitive to the change of illumination conditions, we propose a new type of preprocessing method. Tracking method is applied to reduce the computation time and predict precise face candidate region even though the movement is not uniform. Facial components are also detected using k-means clustering and their geometrical properties. Finally, from the relative distance of two eyes, we verify the real face and estimate the size of facial ellipse. To validate face detection and tracking performance of our algorithm, we test our method using six different video categories of QCIF size which are recorded in dynamic environments.

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Finding faces in color images using wavelet transform

Abstract: Abstract

Cited by 11 publications

References 11 publications

Face Detection: A Survey

Face Detection: A Survey

Skin Color Detection Model Using Neural Networks and its Performance Evaluation

Robust Face Detection and Tracking for Real-Life Applications

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