scholarly journals The Research on the Face Region Detection Based on the Color Characteristics in Mechanics of Materials

IERI Procedia ◽  
2012 ◽  
Vol 3 ◽  
pp. 162-168
Author(s):  
Ma Guiying
Keyword(s):  
Region Detection ◽  
Color Characteristics ◽  
Face Region ◽  
Mechanics Of Materials ◽  
The Face

Research and Improvement on the Algorithm of Face Region Detection Based on Skin Color Model

2014 ◽  
Vol 543-547 ◽  
pp. 2702-2705
Author(s):  
Hong Hai Liu ◽  
Xiang Hua Hou
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Algorithm ◽  
Face Image ◽  
Color Model ◽  
Region Detection ◽  
Face Region ◽  
The Face ◽  
Skin Color Model ◽  
Better Than

In face image with complex background, the CbCr skin color region will have offset when considering the illumination change. Therefore, the non-skin color pixels which luminance is less than 80 will be mistaken as skin color pixels and the skin color pixels which luminance is greater than 230 will be mistaken as non-skin color pixels. In order to reduce the misjudgments, an improved skin color model of nonlinear piecewise is put forward in this paper. Firstly, the skin color model of non-piecewise is analyzed and the experimental results show that by this model there is an obvious misjudgment in face detection. Then the skin color model of nonlinear piecewise is mainly analyzed and is demonstrated by mathematics method. A large number of training results show that the skin color model of nonlinear piecewise has better clustering distribution than the skin color model of non-piecewise. At lastly, the face detection algorithm adopting skin color model of nonlinear piecewise is analyzed. The results show that this algorithm is better than the algorithm adopting skin color model of non-piecewise and it makes a good foundation for the application of face image.

Face detection in single and multiple images using different skin color models

2020 ◽  
Vol 13 ◽  
Author(s):  
Manpreet Kaur ◽  
Jasdev Bhatti ◽  
Mohit Kumar Kakkar ◽  
Arun Upmanyu
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Input Image ◽  
Morphological Operations ◽  
Reliability Model ◽  
Multiple Images ◽  
Face Region ◽  
Bounding Box ◽  
The Face ◽  
Precision And Accuracy

Introduction: Face Detection is used in many different steams like video conferencing, human-computer interface, in face detection, and in the database management of image. Therefore, the aim of our paper is to apply Red Green Blue ( Methods: The morphological operations are performed in the face region to a number of pixels as the proposed parameter to check either an input image contains face region or not. Canny edge detection is also used to show the boundaries of a candidate face region, in the end, the face can be shown detected by using bounding box around the face. Results: The reliability model has also been proposed for detecting the faces in single and multiple images. The results of the experiments reflect that the algorithm been proposed performs very well in each model for detecting the faces in single and multiple images and the reliability model provides the best fit by analyzing the precision and accuracy. Moreover Discussion: The calculated results show that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images. Also, the evaluated results by this paper provides the better testing strategies that helps to develop new techniques which leads to an increase in research effectiveness. Conclusion: The calculated value of all parameters is helpful for proving that the proposed algorithm has been performed very well in each model for detecting the face by using a bounding box around the face in single as well as multiple images. The precision and accuracy of all three models are analyzed through the reliability model. The comparison calculated in this paper reflects that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images.

Face Image Segmentation Using Color Information and Saliency Map

2011 ◽  
Vol 55-57 ◽  
pp. 77-81
Author(s):  
Hui Ming Huang ◽  
He Sheng Liu ◽  
Guo Ping Liu
Keyword(s):  
Image Segmentation ◽  
Human Skin ◽  
Skin Color ◽  
Saliency Map ◽  
Face Image ◽  
Color Information ◽  
Attention Model ◽  
Face Area ◽  
Face Region ◽  
The Face

In this paper, we proposed an efficient method to address the problem of color face image segmentation that is based on color information and saliency map. This method consists of three stages. At first, skin colored regions is detected using a Bayesian model of the human skin color. Then, we get a chroma chart that shows likelihoods of skin colors. This chroma chart is further segmented into skin region that satisfy the homogeneity property of the human skin. The third stage, visual attention model are employed to localize the face region according to the saliency map while the bottom-up approach utilizes both the intensity and color features maps from the test image. Experimental evaluation on test shows that the proposed method is capable of segmenting the face area quite effectively，at the same time, our methods shows good performance for subjects in both simple and complex backgrounds, as well as varying illumination conditions and skin color variances.

scholarly journals State model based face mask detection

2018 ◽  
Vol 7 (2.22) ◽  
pp. 35
Author(s):  
Kavitha M ◽  
Mohamed Mansoor Roomi S ◽  
K Priya ◽  
Bavithra Devi K
Keyword(s):  
Face Mask ◽  
Search Space ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
State Model ◽  
Face Region ◽  
Automatic Teller Machine ◽  
Foreground Region ◽  
The Face ◽  
Surveillance Mechanism

The Automatic Teller Machine plays an important role in the modern economic society. ATM centers are located in remote central which are at high risk due to the increasing crime rate and robbery.These ATM centers assist with surveillance techniques to provide protection. Even after installing the surveillance mechanism, the robbers fool the security system by hiding their face using mask/helmet. Henceforth, an automatic mask detection algorithm is required to, alert when the ATM is at risk. In this work, the Gaussian Mixture Model (GMM) is applied for foreground detection to extract the regions of interest (ROI) i.e. Human being. Face region is acquired from the foreground region through  the torso partitioning and applying Viola-Jones algorithm in this search space. Parts of the face such as Eye pair, Nose, and Mouth are extracted and a state model is developed to detect  mask.  

scholarly journals ID: 1026 Reconstruction of the major defects of the tissue scalp

2017 ◽  
Vol 4 (S) ◽  
pp. 100
Author(s):  
M.I. Muradov ◽  
K.B. Mukhamedkerim ◽  
A. ABaiguzeva ◽  
K.E. Kazantaev ◽  
D.Zh. Koshkarbaev
Keyword(s):  
Donor Site ◽  
Silicone Implants ◽  
Fat Transfer ◽  
Autologous Fat ◽  
Autologous Fat Transfer ◽  
Definitive Evidence ◽  
Face Region ◽  
The Face ◽  
Volume Measurements ◽  
The Mean

Background: To provide quantitative objective data demonstrating the longevity and amount of volume augmentation in the fatty dystrophy of the facial tissue obtained with autologous lipofilling.   Methods: In our clinic had been operated 8 patients for last 2 years with fatty dystrophy of the facial tissue. A prospective analysis of all patients who underwent at our private practice and were followed up for at least 1,5 year. Surgery was performed under general anesthesia it is necessary for clear results tissue correction. We based on the literature has seen numerous clinical reports highlighting the benefits of autologous fat transfer for face from that areas, fat was collected from the abdomen (most frequently used donor site), hips, outer thighs (saddle-bags), internal knee or thigh, with quantitative volume measurements evaluating the amount of postoperative volume change.   Results: Twenty eight patients were included in the study. The mean follow-up time was 18 months. The mean amount of autologous fat injected into each face region was 10-70 mL. Hypercorrection was performed after 3 months and it was 20-50% of the initial injected fat volume. Overall, the mean absolute volume augmentation measured at their last (after 6 month) post operative visit was 10-25%. There was variability between patients in the volume amount and percentage that remained. The resorption process was observed in two patients after 6 month. We made correction with hyaluronic acid and silicone implants.   Conclusion: To our knowledge, this study is the first clinical quantification in our practise of autologous fat transfer and/or grafting in the literature that provides definitive evidence on the amount as well as the resultant longevity in the face. Autologous fat transfer to the face has definite long-term volume augmentation results. On average, approximately 25-35% of the injected volume remains at 18 months. However, some variability exists in the percentage of  volume that remains that may require a touch-up procedure.

scholarly journals Integarted Minimum Cost Sub-Block Matching Distance based Face Recognition using Internet of Things

2019 ◽  
Vol 8 (11) ◽  
pp. 117-122
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Minimum Cost ◽  
Recognition System ◽  
Block Matching ◽  
Face Region ◽  
Input Face ◽  
Face Recognition System ◽  
The Face ◽  
Fuzzy C Means Clustering

Now a days one of the critical factors that affects the recognition performance of any face recognition system is partial occlusion. The paper addresses face recognition in the presence of sunglasses and scarf occlusion. The face recognition approach that we proposed, detects the face region that is not occluded and then uses this region to obtain the face recognition. To segment the occluded and non-occluded parts, adaptive Fuzzy C-Means Clustering is used and for recognition Minimum Cost Sub-Block Matching Distance(MCSBMD) are used. The input face image is divided in to number of sub blocks and each block is checked if occlusion present or not and only from non-occluded blocks MWLBP features are extracted and are used for classification. Experiment results shows our method is giving promising results when compared to the other conventional techniques.

scholarly journals IDENTIFIKASI DAN HABITAT GURITA (CEPHALOPODA) DARI PERAIRAN SALIBABU, KABUPATEN KEPULAUAN TALAUD

2019 ◽  
Vol 7 (3) ◽  
pp. 247
Author(s):  
Andika R Balansada ◽  
Medy Ompi ◽  
Frans Lumoindong
Keyword(s):  
Basic Information ◽  
Color Characteristics ◽  
The Face ◽  
Left And Right

The octopus in Manado language is called Boboca, while the local Talaud community is called Urrita. Octopus is used as food and bait. Information on octopus biology needs to be known as basic information in the management of octopus resources. This study aims to identify and provide information on octopus habitat in the waters of Salibabu. Collecting specimens using arrows (jubi). The morphology of the example octopus is identified as Octopus cyanea Gray, 1849. In the arms of the octopus there are white-colored spots. On the left and right side of the crown of the arm are two false eyes (ocellus). On the face of the ventral arm is a dark pole pattern above the pale or creamy base color. Characteristics of female morphomes generally have a larger size compared to males. Specimen habitats are found outside the nest at night and in the nest during the day time.Keywoeds: Octopus, Biology, Identify, Morphology, Morphometric, Habitat

scholarly journals Liveness Measurements Using Optical Flow for Biometric Person Authentication

2012 ◽  
Vol 19 (2) ◽  
pp. 257-268 ◽  
Author(s):  
Maciej Smiatacz
Keyword(s):  
Optical Flow ◽  
Video Sequences ◽  
Support Vector ◽  
Biometric Identification ◽  
Behavioral Characteristics ◽  
Security Level ◽  
Identification Algorithm ◽  
Face Region ◽  
The Face ◽  
Consumer Devices

Liveness Measurements Using Optical Flow for Biometric Person Authentication Biometric identification systems, i.e. the systems that are able to recognize humans by analyzing their physiological or behavioral characteristics, have gained a lot of interest in recent years. They can be used to raise the security level in certain institutions or can be treated as a convenient replacement for PINs and passwords for regular users. Automatic face recognition is one of the most popular biometric technologies, widely used even by many low-end consumer devices such as netbooks. However, even the most accurate face identification algorithm would be useless if it could be cheated by presenting a photograph of a person instead of the real face. Therefore, the proper liveness measurement is extremely important. In this paper we present a method that differentiates between video sequences showing real persons and their photographs. First we calculate the optical flow of the face region using the Farnebäck algorithm. Then we convert the motion information into images and perform the initial data selection. Finally, we apply the Support Vector Machine to distinguish between real faces and photographs. The experimental results confirm that the proposed approach could be successfully applied in practice.

A Robust and Efficient Face Recognition System Based on Luminance Distribution Using Maximum Likelihood

2012 ◽  
Vol 241-244 ◽  
pp. 1705-1709
Author(s):  
Ching Tang Hsieh ◽  
Chia Shing Hu
Keyword(s):  
Face Recognition ◽  
Maximum Likelihood ◽  
Recognition Rate ◽  
Computation Time ◽  
Likelihood Estimation ◽  
Recognition System ◽  
Luminance Distribution ◽  
Face Region ◽  
Face Recognition System ◽  
The Face

In this paper, a robust and efficient face recognition system based on luminance distribution by using maximum likelihood estimation is proposed. The distribution of luminance components of the face region is acquired and applied to maximum likelihood test for face matching. The experimental results showed that the proposed method has a high recognition rate and requires less computation time.

Human Face Region Detection Driving Activity Recognition in Video

2018 ◽  
pp. 2102-2123
Author(s):  
Anastasios Doulamis ◽  
Athanasios Voulodimos ◽  
Theodora Varvarigou
Keyword(s):  
Activity Recognition ◽  
Human Face ◽  
Multiple Streams ◽  
Body Movements ◽  
Video Signals ◽  
Region Detection ◽  
Face Region ◽  
Classification Framework ◽  
Fusion Methods ◽  
Tremendous Impact

Automatic recognition of human actions from video signals is probably one of the most salient research topics of computer vision with a tremendous impact for many applications. In this chapter, the authors introduce a new descriptor, the Human Constrained Pixel Change History (HC-PCH), which is based on PCH but focuses on the human body movements over time. They propose a modification of the conventional PCH that entails the calculation of two probabilistic maps based on human face and body detection, respectively. These HC-PCH features are used as input to an HMM-based classification framework, which exploits redundant information from multiple streams by employing sophisticated fusion methods, resulting in enhanced activity recognition rates.

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