Color Image Discriminant Models and Algorithms for Face Recognition

Yang, Jian; Liu, Chengjun

doi:10.1109/tnn.2008.2003187

Cited by 97 publications

(47 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the CID method, we implemented its extended version based on the RGB color space [2]. In addition, following the same parameter values as used in [2], the initial value of the CID algorithm and convergence threshold were set to '[1/3,1/3,1/3]' and '0.1', respectively.…”

Section: Evaluation Of Our Methods Under Different Challengesmentioning

confidence: 99%

“…Recently, considerable research work in face recognition (FR) has shown that facial color information can be used to considerably improve FR performance, compared to the FR methods relying only on grayscale information [1][2][3][4][5][6][7]. In particular, it has been reported in [5][6] that the effectiveness of color information can become significant for improving FR performance when face images are taken under strong variations in illumination, as well as with low spatial resolutions.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, currently used color-component choices are mostly made through a combination of intuition and empirical comparison [1][2][3], [5][6][7] without any systematic selection strategy. As such, existing methods may have a limitation to attaining the best FR result for given FR task.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Boosting Color Feature Selection for Color Face Recognition

Choi

Plataniotis

2011

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

This paper introduces the new color face recognition (FR) method that makes effective use of boosting learning as color-component feature selection framework. The proposed boosting color-component feature selection framework is designed for finding the best set of color-component features from various color spaces (or models), aiming to achieve the best FR performance for a given FR task. In addition, to facilitate the complementary effect of the selected color-component features for the purpose of color FR, they are combined using the proposed weighted feature fusion scheme. The effectiveness of our color FR method has been successfully evaluated on the following five public face databases (DBs): CMU-PIE, Color FERET, XM2VTSDB, SCface, and FRGC 2.0. Experimental results show that the results of the proposed method are impressively better than the results of other state-of-the-art color FR methods over different FR challenges including highly uncontrolled illumination, moderate pose variation, and small resolution face images.

show abstract

Section: Evaluation Of Our Methods Under Different Challengesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Boosting Color Feature Selection for Color Face Recognition

Choi

Plataniotis

2011

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…Facial analysis can be used with context based to come out with different new features in HCI which will have lots of commercial applications. Example for this context based gesture analysis [28] is [15].The objective of the proposed work is to detect and recognize various hand gestures and special hand gestures that are present in a given image and use for automation. This Hand Gesture Recognition technique is fast (easy to calculate the orientation histograms), robust to illumination changes and takes less processing time due to little training.…”

Section: Context Based Workingmentioning

confidence: 99%

“…Facial analysis is a sub domain of the main problem in the computer vision i.e. making computer to imitate human eye [28]. This facial analysis can be a good step solves that problem.…”

Section: Improving Computer Visionmentioning

confidence: 99%

Recognition of Facial Gestures using Gabor Filter

Narayan¹,

Paramasivam²,

Ramiah³

2011

IJCA

View full text Add to dashboard Cite

Vision is the task of "seeing". When human see things, their eyes (sensing device) capture the image, then pass the information to brain (interpreting device). The brain interprets the image, gives us meanings of what human see [35]. Similarly, in computer vision, camera serves as sensing device, and computer acts as interpreting device to interpret the image what the camera captures. Gestures are expressive meaningful body motions i.e., physical movements of the hands, arms, fingers, head, face or other parts of the body with the intent to convey information or interact with the environment [12]. Gestures are used for everything from pointing at a person or an object to change the focus of attention, to conveying information. Gestures, which function independently of speech, are referred to as autonomous gestures Autonomous gesture can also represent motion commands to use in communication [15] and machine control. Gesture recognition is the process by which gestures made by the user are made known to the intelligence system. The core objective of the proposed work is to detect and recognize various facial gestures that are present in a given image using Gabor filter and use it for automation. The performance of the proposed method is evaluated using Gabor filtering and compared with the other methods namely wavelet and neural networks. Finally, it is concluded that the proposed method shows better performance over the other methods.

show abstract

Tensor Methods in Computer Vision

Cyganek

2013

Object Detection and Recognition in Digital Images

View full text Add to dashboard Cite

Color Image Discriminant Models and Algorithms for Face Recognition

Cited by 97 publications

References 36 publications

Boosting Color Feature Selection for Color Face Recognition

Boosting Color Feature Selection for Color Face Recognition

Recognition of Facial Gestures using Gabor Filter

Tensor Methods in Computer Vision

Contact Info

Product

Resources

About