Multi-face Detection Based on Improved Gaussian Distribution

Liu, Zaiying; Sha, Jie; Yang, Ping

doi:10.1109/ihmsc.2013.20

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…Finally, 2 conditions of aspect ratio must be fulfilled to classify face or non-face. The author in [6] proposed to use skin-colour model (RGB colour space), facial features (labial feature and holes feature) and improved Gaussian distribution model to detect multiple faces with good performance and remove skin colour-like background. Another challenge of skin colour is to resolve illumination problem.…”

Section: Related Workmentioning

confidence: 99%

The Utilization of Feature based Viola-Jones Method for Face Detection in Invariant Rotation

Soon¹,

Basari²,

Ibrahim³

et al. 2018

ijacsa

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Faces in an image consists of complex structures in object detection. The components of a face, which includes the eyes, nose and mouth of a person differs from that of ordinary objects, thus making face detecting a complex process. Some of the challenges encounter posed in face detection of unconstrained images includes background variation, pose variation, facial expression, occlusion and noise. Current research of Viola-Jones (V-J) face detection is limited to only 45 degrees in-plane rotation. This paper proposes only one technique for the V-J detection face in unconstrained images, which V-J face detection with invariant rotation. The technique begins by rotating the given image file with each step 30 degrees until 360 degrees. Each step of adding 30 degrees from origin, V-J face detection is applied, which covers more angles of a rotated face in unconstrained images. Robust detection in rotation invariant used in the above techniques will aid in the detecting of rotated faces in images. The images that have been utilized for testing and evaluation in this paper are from CMU dataset with 12 rotations on each image. Therefore, there are 12 test patterns generated. These images have been measured through the correct detection rate, true positive and false positive. This paper shows that the proposed V-J face detection technique in unconstrained images have the ability to detect rotated faces with high accuracy in correct detection rate. To summarize, V-J face detection in unconstrained images with proposed variation of rotation is the method utilized in this paper. This proposed enhancement improves the current V-J face detection method and further increase the accuracy of face detection in unconstrained images.

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Section: Related Workmentioning

confidence: 99%

The Utilization of Feature based Viola-Jones Method for Face Detection in Invariant Rotation

Soon¹,

Basari²,

Ibrahim³

et al. 2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…Dataset [195,99] VI. APPLICATIONS Skin segmentation technology is useful and sometimes substantial in wide range of biometric systems infolding face detection/tracking/recognition [36,179,234,145,248], pedestrian detection and tracking [131,256,257], gesture segmentation/recognition [6,29,31], content based image retrieval (CBIR) [5,21,72,121], biomedical imaging [125,258], surveillance systems [247], gaming interfaces [259], access control [95], video conferencing [113], human computer interaction (HCI) technology [243,229], detection of anchors in TV news videos for the sake of automatic annotation [72,253], robotics [8], content aware video compression [260], image color balancing [125,229], steganography [27,125], skin color reproduction [139], video phone or sign language recognition [9,109,243], and anti-spoofing [208,210].…”

Section: Performance Comparisonmentioning

confidence: 99%

A Comprehensive Survey on Human Skin Detection

Mahmoodi

Sayedi

2016

IJIGSP

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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.

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