Face Extraction Using Skin Color and PCA Face Recognition in a Mobile Cloudlet Environment

2016 ◽  
Author(s):  
V.M. Praseetha ◽  
S. Vadivel
Keyword(s):  
Face Recognition ◽  
Skin Color

The System of Face Recognition Design

2014 ◽  
Vol 644-650 ◽  
pp. 3943-3946
Author(s):  
Xiao Bin Yu ◽  
Zi Qiao Li ◽  
Wen Qiang Ke ◽  
Rui Peng Li ◽  
Kai Xiong
Keyword(s):  
Face Recognition ◽  
Skin Color ◽  
Color Space ◽  
Color Model ◽  
Face Images ◽  
Accurate Detection ◽  
Haar Features ◽  
The Face ◽  
The Media ◽  
Skin Color Model

The technology of face recognition is the media to face images as the identity of the face recognition system.Through the choice of color space and the establishment of skin color model, give a rough detection for the human's image, then use the face Haar features getting more accurate detection.

scholarly journals A Novel Fusion Method by Static and Moving Facial Capture

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Shuai Liu ◽  
Weina Fu ◽  
Wenshuo Zhao ◽  
Jiantao Zhou ◽  
Qianzhong Li
Keyword(s):  
Pattern Recognition ◽  
Face Recognition ◽  
Skin Color ◽  
Moving Object ◽  
Experimental Results ◽  
Fusion Method ◽  
Performance Requirement ◽  
Capture Method ◽  
Continuous Frames

For many years, face recognition has been one of the most important domains in pattern recognition. Nowadays, face recognition is more required to be used in video actually. So moving facial capture must be studied firstly because of performance requirement. Since classic facial capture method is not so suitable in a moving environment, in this paper, we present a novel facial capture method in a moving environment. Firstly, continuous frames are extracted from detecting videos by similar characteristics. Then, we present an algorithm to extract the moving object and restructure background. Meanwhile, with analysis of skin color in both moving and static areas, we use the classic faces capture method to catch all faces. Finally, experimental results show that this method has better robustness and accuracy.

scholarly journals ANALYSIS OF HUMAN FACE RECOGNITION ALGORITHM USING PCA+FDIT IN IMAGE DATABASE FOR CRIME INVESTIGATION

2013 ◽  
Vol 4 (3) ◽  
pp. 788-796
Author(s):  
V. S. Manjula
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Face Detection ◽  
Skin Color ◽  
Back Propagation ◽  
Real Life ◽  
Hierarchical Architecture ◽  
Detection And Identification ◽  
The Face ◽  
Real Time Application

In general, the field of face recognition has lots of research that have put interest in order to detect the face and to identify it and also to track it. Many researchers have concentrated on the face identification and detection problem by using various approaches. The proposed approach is further very useful and helpful in real time application. Thus the Face Detection, Identification  which is proposed here is used to detect the faces in videos in the real time application by using the FDIT (Face Detection Identification Technique) algorithm. Thus the proposed mechanism is very help full in identifying individual persons who are been involved in the action of robbery, murder cases and terror activities. Although in face recognition the algorithm used is of histogram equalization combined with Back propagation neural network in which we recognize an unknown test image by comparing it with the known training set images that are been stored in the database. Also the proposed approach uses skin color extraction as a parameter for face detection. A multi linear training and rectangular face feature extraction are done for training, identifying and detecting.   Thus the proposed technique   is PCA + FDIT technique configuration only improved recognition for subjects in images are included in the training data.   It is very useful in identify a single person from a group of faces.   Thus the proposed technique is well suited for all kinds faces frame work for face detection and identification. The face detection and identification modules share the same hierarchical architecture. They both consist of two layers of classifiers, a layer with a set of component classifiers and a layer with a single combination classifier.  Also we have taken a real life example and simulated the algorithms in IDL Tool successfully.

scholarly journals ASL characters recognition using person specific subspace method

1970 ◽  
Vol 4 (2) ◽  
pp. 39-52
Author(s):  
Md Hasanuzzaman ◽  
SM Tareeq ◽  
MA Bhuiyan ◽  
Y Shirai ◽  
H Ueno
Keyword(s):  
Face Recognition ◽  
Key Words ◽  
Classification Accuracy ◽  
Skin Color ◽  
Color Segmentation ◽  
Subspace Method ◽  
Segmentation Method ◽  
Pca Method ◽  
International University ◽  
Better Than

This paper presents a person-specific subspace method for ASL characters recognition. To implement this person specific subspace method for ASL characters recognition the system first recognizes the faces and then uses skin-color segmentation method for hand poses segmentation. From the segmented hand poses the system recognizes ASL characters using both PCA and person-specific subspace method separately. Experiment result shows that the classification accuracy of person-specific subspace method is better than general PCA method. The experiment result also shows that person-specific subspace method is faster than general PCA method. Key Words: ASL character, Face recognition, PCA, Person-specific subspace method DOI: 10.3329/diujst.v4i2.4366 Daffodil International University Journal of Science and Technology Vol.4(2) 2009 pp.39-52

Face Recognition using Fast Fourier Transform

2016 ◽  
pp. 302-322
Author(s):  
Shivakumar Baragi ◽  
Nalini C. Iyer
Keyword(s):  
Face Recognition ◽  
Skin Color ◽  
Color Image ◽  
Rejection Rate ◽  
Threshold Value ◽  
Recognition System ◽  
Mean Value ◽  
Test Image ◽  
The Matrix ◽  
Test Database

Biometrics refers to metrics related to human characteristics and Traits. Face Recognition is the process of identification of a person by their facial image. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. The objective is to authenticate a person, to have a FAR and FRR very low. This project introduces a new approach for face recognition system using FFT algorithm. The database that contains the images is named as train database and the test image which is stored in test database is compared with the created train database. For further processing RGB data is converted into grayscale, thus reduces the matrix dimension. FFT is applied to the entire database and mean value of the images is computed and the same is repeated on test database also. Based on the threshold value of the test image, face recognition is done. Performance evaluation of Biometrics is done for normal image, skin color image, ageing image and blur image using False Acceptance Rate(FAR), False Rejection Rate(FRR), Equal Error Rate(EER) and also calculated the accuracy of different images.

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