Human face detection using skin color information

Shehadeh, Hakam; Al-khalaf, Audai; Al-khassaweneh, Mahmood

doi:10.1109/eit.2010.5612128

Cited by 7 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its value will decrease (from the circular to the oval shape) as a person becomes older. We extract "CirFace" from color or grey-scale face images as proposed in [30]. The calculation of "CirFace" involves three steps: skin detection, edge detection, and modification.…”

Section: Shape-based Classifiermentioning

confidence: 99%

Age group classification via structured fusion of uncertainty-driven shape features and selected surface features

Liu

Yan

Kuo

2014

IEEE Winter Conference on Applications of Computer Vision

View full text Add to dashboard Cite

In this paper, we present a structured fusion method for facial age group classification. To utilize the structured fusion of shape features and surface features, we introduced the region of certainty (ROC) to not only control the classification accuracy for shape feature based system but also reduce the classification needs on surface feature based system. In the first stage, we design two shape features, which can be used to classify frontal faces with high accuracies. In the second stage, a surface feature is adopted and then selected by a statistical method. The statistical selected surface features combined with a SVM classifier can offer high classification rates. With properly adjusting the ROC by a single non-sensitive parameter, the structured fusion of two stages can provide a performance improvement. In the experiments, we use face images in the public available FG-NET and MORPH databases and partition them into three pre-defined age groups. It is observed that the proposed method offers a correct classification rate of 95.1% in FG-NET and 93.7% in MORPH, which outperforms state-ofthe-art methods by a significant margin.

show abstract

Section: Shape-based Classifiermentioning

confidence: 99%

Age group classification via structured fusion of uncertainty-driven shape features and selected surface features

Liu

Yan

Kuo

2014

IEEE Winter Conference on Applications of Computer Vision

View full text Add to dashboard Cite

show abstract

“…Circularity in (8) [17] is an indicator to describe how circular of an object is. The more circular shape the object has the lower circularity value the object is.…”

Section: B Circularity Testmentioning

confidence: 99%

Development of a geometrical algorithm for eye detection in color images

Phromsuthirak

Umchid

2012

The 5th 2012 Biomedical Engineering International Conference

View full text Add to dashboard Cite

The applications of eye detection have grown significantly during the past decade. The objective of this paper was to develop a new technique for eye detection in color facial images. The implementation of this technique was composed of three steps. First step is using skin color information to detect a face in color images. The face region would be segmented from the background. Next step, illumination based method (chrominance components and luma component) was employed to find the possible location of the eyes. Finally, to locate the exact position of the eyes, the flexible thresholding method and new eye geometrical tests were utilized. The proposed technique was tested using Aberdeen facial images from Psychological Image Collection at Stirling (PICS image database). The simulation results indicate the detection rate of 93.2% for frontal facial images and 85.7% for rotational facial images.

show abstract

“…If there is a center point then stop the scanning, and regard the present skin color center pixel as the starting of the face detection. Shehadeh et al [7] proposed system which computes the difference between each vector in the reference matrix and pixels in the input image. If the difference is less than a threshold then this pixel is set to be skin.…”

Section: Related Workmentioning

confidence: 99%

Mingle Face Detection using Adaptive Thresholding and Hybrid Median Filter

Kaur¹

2013

IJCA

View full text Add to dashboard Cite

Face detection has many applications in the field of digital image processing like face recognition, face analysis etc. Various techniques have been proposed so far to detect faces in images by different researchers. This paper presents a noble face detection algorithm which integrates the adaptive segmentation and hybrid median filter to improve the performance of face detection algorithms. Single faces noisy images are considered in this research work to evaluate the performance of the proposed algorithm. The results are compared with some well known face detection algorithms.

show abstract

Human face detection using skin color information

Cited by 7 publications

References 2 publications

Age group classification via structured fusion of uncertainty-driven shape features and selected surface features

Age group classification via structured fusion of uncertainty-driven shape features and selected surface features

Development of a geometrical algorithm for eye detection in color images

Mingle Face Detection using Adaptive Thresholding and Hybrid Median Filter

Contact Info

Product

Resources

About