Segmentation-based illumination normalization for face detection

Yao, Min; Aoki, Kota; Nagahashi, Hiroshi

doi:10.1109/iwcia.2013.6624793

Cited by 9 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conci et al [250] employed spectral variation coefficient (SVC) texture tool in order to distinguish skin regions. A relatively computationally expensive algorithm is used to calculate the SVC.…”

Section: H Mixuret Methodsmentioning

confidence: 99%

A Comprehensive Survey on Human Skin Detection

Mahmoodi

Sayedi

2016

IJIGSP

View full text Add to dashboard Cite

Human Skin detection is one of the most widely used algorithms in vision literature which has been numerously exploited both directly and indirectly in multifarious applications. This scope has received a great deal of attention specifically in face analysis and human detection/tracking/recognition systems. As regards, there are several challenges mainly emanating from nonlinear illumination, camera characteristics, imaging conditions, and intra-personal features. During last twenty years, researchers have been struggling to overcome these challenges resulting in publishing hundreds of papers. The aim of this paper is to survey applications, color spaces, methods and their performances, compensation techniques and benchmarking datasets on human skin detection topic, covering the related researches within more than last two decades. In this paper, different difficulties and challenges involved in the task of finding skin pixels are discussed. Skin segmentation algorithms are mainly based on color information; an in-depth discussion on effectiveness of disparate color spaces is elucidated. In addition, using standard evaluation metrics and datasets make the comparison of methods both possible and reasonable. These databases and metrics are investigated and suggested for future studies. Reviewing most existing techniques not only will ease future studies, but it will also result in developing better methods. These methods are classified and illustrated in detail. Variety of applications in which skin detection has been either fully or partially used is also provided.

show abstract

Section: H Mixuret Methodsmentioning

confidence: 99%

A Comprehensive Survey on Human Skin Detection

Mahmoodi

Sayedi

2016

IJIGSP

View full text Add to dashboard Cite

show abstract

“…In an attempt to boost performance, illumination normalisation has been applied to Viola–Jones face detection [6] and more efficient ways of convolving Haar‐like features have been explored [7]. Haar features with AdaBoost have been used to locate facial image feature points [8], while the technique of rotating detected sub‐windows to handle rotated faces in the image has been effective [9].…”

Section: Introductionmentioning

confidence: 99%

Face detection with a Viola–Jones based hybrid network

et al. 2016

View full text Add to dashboard Cite

Face detection is the determination of the positions and sizes of faces, primarily human, within digital images and videos, often as a component of a broader facial recognition system. It is seen as technologically mature, yet its operational performance typically remains sub-optimal, even within the less difficult frontal face detection tests. Empirical evidence shows that the Viola-Jones framework, a standard face detection solution with generally superior performance and other desirable properties, underdetects in some instances. Some true faces survive all but the final stages of the rejection cascade, resulting in missed faces. A hybrid framework consisting of a neural network following a truncated Viola-Jones cascade is constructed in an attempt to recover the undetected faces. Presumably, the neural network could fine tune and augment the face decision. Its inputs are a subset of the thresholding (detection) values of a rejection cascade's intermediate stages. Experiments reveal significantly improved performance, with increased detection rates if no false alarm increases are tolerated, with a greater detection rate increase if some false alarm increases are acceptable, and with a substantial false alarm reduction with no detection reduction. These improved face detection results could address shortcomings in widely-varying applications.

show abstract

“…But, this can be costly [5]. Dynamic models [10], illumination correction algorithms [11][12][13][14][15] and homomorphic filtering [16][17][18] are other solutions along with their pros and cons. For example, homomorphic filtering is not directly applicable for color images and it is very slow.…”

Section: Introductionmentioning

confidence: 99%

Boosting performance of face detection by using an efficient skin segmentation algorithm

Mahmoodi

Sayedi

2014

2014 6th International Conference on Information Technology and Electrical Engineering (ICITEE)

View full text Add to dashboard Cite

Skin detection plays a very essential role in many image processing applications such as face localization, face recognition, gesture recognition and human identification. A robust pre-processing skin detection algorithm can significantly increase the performance of an application in both terms of speed and accuracy. Skin segmentation is often computationally simple, though in many conditions, uneven and nonlinear illumination degrades its performance. Recently, many methods have been proposed to solve the problem, yet most of them are not applicable in different conditions and disparate illuminations. This paper proposes an algorithm which employs not only a pixel-based processing, but also an additional neighbor-based and region-based processing. This enhances the output of the algorithm. Also a novel method in which the YC b C r image is converted to a ternary image by means of a set of decisive rules is employed. In a further processing stage, a region-based algorithm is used to decide with a high certainly on the pixels which are not skin. Results demonstrate that the proposed algorithm is robust and capable of detecting skin windows comparable with state of the art algorithms.

show abstract

Segmentation-based illumination normalization for face detection

Cited by 9 publications

References 18 publications

A Comprehensive Survey on Human Skin Detection

A Comprehensive Survey on Human Skin Detection

Face detection with a Viola–Jones based hybrid network

Boosting performance of face detection by using an efficient skin segmentation algorithm

Contact Info

Product

Resources

About