Multiple face detection algorithm using colour skin modelling

Amjad, Anas; Griffiths, Alison; Patwary, Mohammad

doi:10.1049/iet-ipr.2012.0167

Cited by 17 publications

(3 citation statements)

References 22 publications

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“…In the YCbCr and HSV colour spaces, the performance of skin clustering is poor in Y channel and V channel. Furthermore, rgb, YCbCr, and HSV are more widely used in skin detection [32, 33]. On the basis of the above reasons, hybrid colour space may contain colour channels: r , g , H , S , Cb , and Cr .…”

Section: Resultsmentioning

confidence: 99%

Adaptive skin detection using face location and facial structure estimation

Luo

Guan

2017

IET Computer Vision

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Reliable and accurate facial skin extraction is the most critical and urgent issue for adaptive skin detection. Aiming at resolving this issue, the authors propose an adaptive skin detection method using face location and facial structure estimation. The face location algorithm is developed to improve the reliability of face detection and extract a face region with a high proportion of skin. Facial structure estimation is exploited to further reduce the impact of non-skin factors on dynamic skin colour modelling. The colour space distribution model of extracted facial skin is very close to that of real facial skin. Finally, the skin in an image is obtained by using a hybrid colour space strategy. Extensive experimental comparisons with some state-of-the-art methods have shown the superior performance of the proposed method.

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Section: Resultsmentioning

confidence: 99%

Adaptive skin detection using face location and facial structure estimation

Luo

Guan

2017

IET Computer Vision

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“…The pixels probabilities are indexed at their respective locations in the object segmentation matrix S m . The object of interest is extracted from I i by image segmentation using the following equation [23],…”

Section: E Feature Detection and Extractionmentioning

confidence: 99%

Characterization of Field-of-View for Energy Efficient Application-Aware Visual Sensor Networks

et al. 2016

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Energy consumption is one of the primary concerns in a resource constrained Visual Sensor Network. The existing Visual Sensor Network design solutions under particular resource constrained scenarios are application specific; whereas the degree of sensitivity of any of these resource constrains (e.g. energy etc) varies from one application to another. This limits the implementation of the existing energy efficient solutions within a Visual Sensor Network node which may be considered to be a part of a heterogeneous network. The heterogeneity of image capture and processing within a Visual Sensor Network can be adaptively reflected with a dynamic Field-of-View realisation. This is expected to allow the implementation of a generalised energy efficient solution to adapt with the heterogeneity of the network. In this paper, an energy efficient Field-of-View characterisation framework is proposed which can support a diverse range of applications. The context of adaptivity in the proposed Field-of-View characterisation framework is considered to be: a) sensing range selection; b) maximising spatial coverage; c) adaptive task classification and d) minimising the number of required nodes. Soft decision criteria is exploited and it is observed that for a given detection reliability, the proposed framework provides energy efficient solutions which can be implemented within heterogeneous networks. It is also found that the proposed design solution for heterogeneous networks leads to 49.8% energy savings compared to the trivial design solution.

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“…In general, a facial expression recognition system could be divided into several modules [35]. First of all, face detection module [2,20,22,40] is to find the face of the subject from captured image. There might be obstacles that make this detecting process harder due to changes in environments [46], lighting, or face orientations [52].…”

Section: Introductionmentioning

confidence: 99%

Effective semantic features for facial expressions recognition using SVM

Hsieh¹,

Hsih²,

Jiang³

et al. 2015

Multimed Tools Appl

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Most traditional facial expression-recognition systems track facial components such as eyes, eyebrows, and mouth for feature extraction. Though some of these features can provide clues for expression recognition, other finer changes of the facial muscles can also be deployed for classifying various facial expressions. This study locates facial components by active shape model to extract seven dynamic face regions (frown, nose wrinkle, two nasolabial folds, two eyebrows, and mouth). Proposed semantic facial features could then be acquired using directional gradient operators like Gabor filters and Laplacian of Gaussian. A multi-class support vector machine (SVM) was trained to classify six facial expressions (neutral, happiness, surprise, anger, disgust, and fear). The popular Cohn-Kanade database was tested and the average recognition rate reached 94.7 %. Also, 20 persons were invited for on-line test and the recognition rate was about 93 % in a real-world environment. It demonstrated that the proposed semantic facial features could effectively represent changes between facial expressions. The time complexity could be lower than the other SVM based approaches due to the less number of deployed features.

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Multiple face detection algorithm using colour skin modelling

Cited by 17 publications

References 22 publications

Adaptive skin detection using face location and facial structure estimation

Adaptive skin detection using face location and facial structure estimation

Characterization of Field-of-View for Energy Efficient Application-Aware Visual Sensor Networks

Effective semantic features for facial expressions recognition using SVM

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