scholarly journals The Impact of Color Space and Intensity Normalization to Face Detection Performance

2017 ◽  
Vol 15 (4) ◽  
pp. 1895
Author(s):  
I Nyoman Gede Arya Astawa ◽  
I Ketut Gede Darma Putra ◽  
I Made Sudarma ◽  
Rukmi Sari Hartati
Keyword(s):  
Face Detection ◽  
Color Space ◽  
Detection Performance ◽  
The Impact ◽  
Intensity Normalization

scholarly journals Enhancement Image Intensity of HSV Color Space

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2583-2585
Keyword(s):  
Light Intensity ◽  
Face Detection ◽  
Color Image ◽  
Color Space ◽  
Detection Performance ◽  
Lighting Condition ◽  
Hsv Color Space ◽  
Number Of Faces ◽  
Intensity Normalization ◽  
Poor Lighting

One of factor that affects technology in face detecting or recognizing is illumination. Poor lighting can cause difficulty to the system to recognize face. Although it is over exposure or under exposure. By doing color image processing, it supports the system to detect face in a poor lighting condition. This research used lighting intensity normalization method to increase face detection performance. This method can normalize the light intensity especially on the face lighting. We normalize the light intensity through HSV color space. HSV color space has saturation which is amount of light in the image. The method proceed saturation in image to increase face detection performance. Total number of faces we had tested is 286 faces, the system detect 243 faces before intensity normalization proceed. Whereas, after normalization process, it detects more faces which is 279 faces. As we can see, the percentage improvement before to after intensity normalization is 84.97% to 97.55%. This is 12.58% improvement. We can say this method helps face detection to increase it performance.

scholarly journals An Innovative Face Detection Based on YCgCr Color Space

2018 ◽  
Author(s):  
Solly Aryza
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Color Image ◽  
Color Space ◽  
Gaussian Model ◽  
Detection Rates ◽  
Detection Algorithms ◽  
Lighting Conditions ◽  
Wide Range ◽  
Human Faces

It is very challenging to recognize a face from an image due to the wide variety of face and the uncertain of face position. The research on detecting human faces in color image and in video sequence has been attracted with more and more people. In this paper, we propose a novel face detection method that achieves better detection rates. The new face detection algorithms based on skin color model in YCgCr chrominance space. Firstly, we build a skin Gaussian model in Cg-Cr color space. Secondly, a calculation of correlation coefficient is performed between the given template and the candidates. Experimental results demonstrate that our system has achieved high detection rates and low false positives over a wide range of facial variations in color, position and varying lighting conditions.

Evaluation of the Color Space Influence in Face Detection

2010 ◽  
Author(s):  
A.F. Arias ◽  
N.B. Rodríguez ◽  
L.L. Freire ◽  
M.G. Penedo
Keyword(s):  
Face Detection ◽  
Color Space

scholarly journals SE-IYOLOV3: An Accurate Small Scale Face Detector for Outdoor Security

Mathematics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 93 ◽  
Author(s):  
Zhenrong Deng ◽  
Rui Yang ◽  
Rushi Lan ◽  
Zhenbing Liu ◽  
Xiaonan Luo
Keyword(s):  
Receptive Field ◽  
Face Detection ◽  
Network Structure ◽  
Difficult Problem ◽  
Detection Performance ◽  
Experimental Results ◽  
Small Scale ◽  
Detection Accuracy ◽  
Novel Method ◽  
Face Detector

Small scale face detection is a very difficult problem. In order to achieve a higher detection accuracy, we propose a novel method, termed SE-IYOLOV3, for small scale face in this work. In SE-IYOLOV3, we improve the YOLOV3 first, in which the anchorage box with a higher average intersection ratio is obtained by combining niche technology on the basis of the k-means algorithm. An upsampling scale is added to form a face network structure that is suitable for detecting dense small scale faces. The number of prediction boxes is five times more than the YOLOV3 network. To further improve the detection performance, we adopt the SENet structure to enhance the global receptive field of the network. The experimental results on the WIDERFACEdataset show that the IYOLOV3 network embedded in the SENet structure can significantly improve the detection accuracy of dense small scale faces.

scholarly journals The Geometry of Noise in Color and Spectral Image Sensors

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4487
Author(s):  
Axel Clouet ◽  
Jérôme Vaillant ◽  
David Alleysson
Keyword(s):  
Signal To Noise Ratio ◽  
Spectral Estimation ◽  
Spectral Characteristics ◽  
Color Space ◽  
Color Correction ◽  
Signal To Noise ◽  
Noise Evaluation ◽  
Noise Ratio ◽  
Correction Step ◽  
The Impact

Digital images are always affected by noise and the reduction of its impact is an active field of research. Noise due to random photon fall onto the sensor is unavoidable but could be amplified by the camera image processing such as in the color correction step. Color correction is expressed as the combination of a spectral estimation and a computation of color coordinates in a display color space. Then we use geometry to depict raw, spectral and color signals and noise. Geometry is calibrated on the physics of image acquisition and spectral characteristics of the sensor to study the impact of the sensor space metric on noise amplification. Since spectral channels are non-orthogonal, we introduce the contravariant signal to noise ratio for noise evaluation at spectral reconstruction level. Having definitions of signal to noise ratio for each steps of spectral or color reconstruction, we compare performances of different types of sensors (RGB, RGBW, RGBWir, CMY, RYB, RGBC).

Contribution of Green Jadeite-Jade’s Chroma Difference Based on CIE 1976 L*a*b* Uniform Color Space

2010 ◽  
Vol 177 ◽  
pp. 620-623 ◽  
Author(s):  
Ying Guo ◽  
Jun Zhang ◽  
Tao Mo
Keyword(s):  
Color Space ◽  
Color Perception ◽  
Correlation Coefficients ◽  
Color Difference ◽  
Green Color ◽  
Difference Formula ◽  
Positive Linear Correlation ◽  
Cie Lab ◽  
Human Eyes ◽  
The Impact

The correlations between lightness and chroma, lightness difference and color difference, chroma difference and color difference were studied to evaluate the impact of lightness on color. Based on color difference formula CIE LAB in the uniform color space CIE L*a*b* it is learnt that H*ab of jadeite jade green colors has made little contribution to E*ab. Given the fact that human eyes are relatively sensitive to the color perception of lightness difference and that lightness and chroma affect each other, lightness of jadeites has been divided into two groups: while the lightness of green is relatively low (L*  19.52), lightness and chroma have positive linear correlation (correlation coefficient L*  C* = 0.971), which means the higher lightness the higher chroma and brings brighter green color; while L* > 19.52 , there is no one-to-one correspondence between lightness and chroma, and the highest chroma 77.64 can be reached when L* = 37.63. The high partial correlation coefficients L*ab  E*ab = 0.974 and C*ab  E*ab = 0.971 reveal that both L*ab and C*ab are not affected by the lightness of jadeite and are equally important to E*ab. It is concluded that the quality estimation of green color of Jadeite Jade should be primarily based on lightness which is the most intuitive factor and consistent with the color perception, and then followed by the evaluation of chroma and hue.

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