scholarly journals Face Recognition Approach Based on the Integration of Image Preprocessing, CMLABP and PCA Methods

2017 ◽  
Vol 13 (1) ◽  
pp. 104-113
Author(s):  
Yaqeen Mezaal
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Input Image ◽  
Birth Date ◽  
Image Preprocessing ◽  
Face Recognition System ◽  
Proposed Model ◽  
Occurrence Matrix ◽  
Local Average

Face recognition technique is an automatic approach for recognizing a person from digital images using mathematical interpolation as matrices for these images. It can be adopted to realize facial appearance in the situations of different poses, facial expressions, ageing and other changes. This paper presents efficient face recognition model based on the integration of image preprocessing, Co-occurrence Matrix of Local Average Binary Pattern (CMLABP) and Principle Component Analysis (PCA) methods respectively. The proposed model can be used to compare the input image with existing database images in order to display or record the citizen information such as name, surname, birth date, etc. The recognition rate of the model is better than 99%. Accordingly, the proposed face recognition system is functional for criminal investigations. Furthermore, it has been compared with other reported works in the literature using diverse databases and training images.

scholarly journals Face Recognition Approach Based on the Integration of Image Preprocessing, CMLABP and PCA Methods

2017 ◽  
Vol 13 (1) ◽  
pp. 104-113
Author(s):  
Yaqeen Mezaal
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Input Image ◽  
Birth Date ◽  
Image Preprocessing ◽  
Face Recognition System ◽  
Proposed Model ◽  
Occurrence Matrix ◽  
Local Average

Face recognition technique is an automatic approach for recognizing a person from digital images using mathematical interpolation as matrices for these images. It can be adopted to realize facial appearance in the situations of different poses, facial expressions, ageing and other changes. This paper presents efficient face recognition model based on the integration of image preprocessing, Co-occurrence Matrix of Local Average Binary Pattern (CMLABP) and Principle Component Analysis (PCA) methods respectively. The proposed model can be used to compare the input image with existing database images in order to display or record the citizen information such as name, surname, birth date, etc. The recognition rate of the model is better than 99%. Accordingly, the proposed face recognition system is functional for criminal investigations. Furthermore, it has been compared with other reported works in the literature using diverse databases and training images.

Face Recognition Based on Symmetrical Half-Join Method using Stereo Vision Camera

2016 ◽  
Vol 6 (6) ◽  
pp. 2818 ◽  
Author(s):  
Edy Winarno ◽  
Agus Harjoko ◽  
Aniati Murni Arymurthy ◽  
Edi Winarko
Keyword(s):  
Face Recognition ◽  
Stereo Vision ◽  
Recognition Rate ◽  
Recognition System ◽  
Face Images ◽  
Face Recognition System ◽  
Proposed Model ◽  
The Face ◽  
Left And Right ◽  
Recognition Technique

<p>The main problem in face recognition system based on half-face pattern is how to anticipate poses and illuminance variations to improve recognition rate. To solve this problem, we can use two lenses on stereo vision camera in face recognition system. Stereo vision camera has left and right lenses that can be used to produce a 2D image of each lens. Stereo vision camera in face recognition has capability to produce two of 2D face images with a different angle. Both angle of the face image will produce a detailed image of the face and better lighting levels on each of the left and right lenses. In this study, we proposed a face recognition technique, using 2 lens on a stereo vision camera namely symmetrical half-join. Symmetrical half-join is a method of normalizing the image of the face detection on each of the left and right lenses in stereo vision camera, then cropping and merging at each image. Tests on face recognition rate based on the variety of poses and variations in illumination shows that the symmetrical half-join method is able to provide a high accuracy of face recognition and can anticipate variations in given pose and illumination variations. The proposed model is able to produce 86% -97% recognition rate on a variety of poses and variations in angles between 0 °- 22.5 °. The variation of illuminance measured using a lux meter can result in 90% -100% recognition rate for the category of at least dim lighting levels (above 10 lux).</p>

scholarly journals Face Recognition Based on Symmetrical Half-Join Method using Stereo Vision Camera

2016 ◽  
Vol 6 (6) ◽  
pp. 2818
Author(s):  
Edy Winarno ◽  
Agus Harjoko ◽  
Aniati Murni Arymurthy ◽  
Edi Winarko
Keyword(s):  
Face Recognition ◽  
Stereo Vision ◽  
Recognition Rate ◽  
Recognition System ◽  
Face Images ◽  
Face Recognition System ◽  
Proposed Model ◽  
The Face ◽  
Left And Right ◽  
Recognition Technique

<p>The main problem in face recognition system based on half-face pattern is how to anticipate poses and illuminance variations to improve recognition rate. To solve this problem, we can use two lenses on stereo vision camera in face recognition system. Stereo vision camera has left and right lenses that can be used to produce a 2D image of each lens. Stereo vision camera in face recognition has capability to produce two of 2D face images with a different angle. Both angle of the face image will produce a detailed image of the face and better lighting levels on each of the left and right lenses. In this study, we proposed a face recognition technique, using 2 lens on a stereo vision camera namely symmetrical half-join. Symmetrical half-join is a method of normalizing the image of the face detection on each of the left and right lenses in stereo vision camera, then cropping and merging at each image. Tests on face recognition rate based on the variety of poses and variations in illumination shows that the symmetrical half-join method is able to provide a high accuracy of face recognition and can anticipate variations in given pose and illumination variations. The proposed model is able to produce 86% -97% recognition rate on a variety of poses and variations in angles between 0 °- 22.5 °. The variation of illuminance measured using a lux meter can result in 90% -100% recognition rate for the category of at least dim lighting levels (above 10 lux).</p>

Research on Face Recognition Technology of Real-Time Video High Performance Similarity

2015 ◽  
Vol 713-715 ◽  
pp. 2160-2164
Author(s):  
Zhao Nan Yang ◽  
Shu Zhang
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Real Time ◽  
High Performance ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Recognition System ◽  
Image Preprocessing ◽  
Face Images ◽  
Face Recognition System

A new similarity measurement standard is proposed, namely background similarity matching. Learning algorithm based on kernel function is utilized in the method for feature extraction and classification of face image. Meanwhile, a real-time video face recognition method is proposed, image binary algorithm in similarity calculation is introduced, and a video face recognition system is designed and implemented [1-2]. The system is provided with a camera to obtain face images, and face recognition is realized through image preprocessing, face detection and positioning, feature extraction, feature learning and matching. Design, image preprocessing, feature positioning and extraction, face recognition and other major technologies of face recognition systems are introduced in details. Lookup mode from top down is improved, thereby improving lookup accuracy and speed [3-4]. The experimental results showed that the method has high recognition rate. Higher recognition rate still can be obtained even for limited change images of face images and face gesture with slightly uneven illumination. Meanwhile, training speed and recognition speed of the method are very fast, thereby fully meeting real-time requirements of face recognition system [5]. The system has certain face recognition function and can well recognize front faces.

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.

Hybrid feature descriptor and probabilistic neuro-fuzzy system for face recognition

Sensor Review ◽  
2018 ◽  
Vol 38 (3) ◽  
pp. 269-281 ◽  
Author(s):  
Hima Bindu ◽  
Manjunathachari K.
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition System ◽  
Feature Descriptor ◽  
Global Features ◽  
Fuzzy Classifier ◽  
Content Type ◽  
Face Recognition System ◽  
Neuro Fuzzy ◽  
Proposed Model

Purpose This paper aims to develop the Hybrid feature descriptor and probabilistic neuro-fuzzy system for attaining the high accuracy in face recognition system. In recent days, facial recognition (FR) systems play a vital part in several applications such as surveillance, access control and image understanding. Accordingly, various face recognition methods have been developed in the literature, but the applicability of these algorithms is restricted because of unsatisfied accuracy. So, the improvement of face recognition is significantly important for the current trend. Design/methodology/approach This paper proposes a face recognition system through feature extraction and classification. The proposed model extracts the local and the global feature of the image. The local features of the image are extracted using the kernel based scale invariant feature transform (K-SIFT) model and the global features are extracted using the proposed m-Co-HOG model. (Co-HOG: co-occurrence histograms of oriented gradients) The proposed m-Co-HOG model has the properties of the Co-HOG algorithm. The feature vector database contains combined local and the global feature vectors derived using the K-SIFT model and the proposed m-Co-HOG algorithm. This paper proposes a probabilistic neuro-fuzzy classifier system for the finding the identity of the person from the extracted feature vector database. Findings The face images required for the simulation of the proposed work are taken from the CVL database. The simulation considers a total of 114 persons form the CVL database. From the results, it is evident that the proposed model has outperformed the existing models with an improved accuracy of 0.98. The false acceptance rate (FAR) and false rejection rate (FRR) values of the proposed model have a low value of 0.01. Originality/value This paper proposes a face recognition system with proposed m-Co-HOG vector and the hybrid neuro-fuzzy classifier. Feature extraction was based on the proposed m-Co-HOG vector for extracting the global features and the existing K-SIFT model for extracting the local features from the face images. The proposed m-Co-HOG vector utilizes the existing Co-HOG model for feature extraction, along with a new color gradient decomposition method. The major advantage of the proposed m-Co-HOG vector is that it utilizes the color features of the image along with other features during the histogram operation.

scholarly journals Real time Implementation of Face Recognition System on Raspberry Pi

2018 ◽  
Vol 7 (2.17) ◽  
pp. 85
Author(s):  
K Raju ◽  
Dr Y.Srinivasa Rao
Keyword(s):  
Face Recognition ◽  
High Performance ◽  
Recognition Rate ◽  
Cost Effective ◽  
Recognition System ◽  
Raspberry Pi ◽  
Face Recognition System ◽  
Effective System ◽  
Face Detection And Recognition ◽  
Improved Accuracy

Face Recognition is the ability to find and detect a person by their facial attributes. Face is a multi dimensional and thus requires a considerable measure of scientific calculations. Face recognition system is very useful and important for security, law authorization applications, client confirmation and so forth. Hence there is a need for an efficient and cost effective system. There are numerous techniques that are as of now proposed with low Recognition rate and high false alarm rate. Hence the major task of the research is to develop face recognition system with improved accuracy and improved recognition time. Our objective is to implementing Raspberry Pi based face recognition system using conventional face detection and recognition techniques such as A Haar cascade classifier is trained for detection and Local Binary Pattern (LBP) as a feature extraction technique. With the use of the Raspberry Pi kit, we go for influencing the framework with less cost and simple to use, with high performance. 

scholarly journals Performance Improvement of Face Recognition System using Selective Local Feature Vectors

2013 ◽  
Vol 10 (2) ◽  
pp. 1330-1338
Author(s):  
Vasudha S ◽  
Neelamma K. Patil ◽  
Dr. Lokesh R. Boregowda
Keyword(s):  
Face Recognition ◽  
Performance Improvement ◽  
Recognition Rate ◽  
Recognition System ◽  
Image Splicing ◽  
Feature Vectors ◽  
Face Recognition System ◽  
Prime Concern ◽  
Wide Range ◽  
Recognition Systems

Face recognition is one of the important applications of image processing and it has gained significant attention in wide range of law enforcement areas in which security is of prime concern. Although the existing automated machine recognition systems have certain level of maturity but their accomplishments are limited due to real time challenges. Face recognition systems are impressively sensitive to appearance variations due to lighting, expression and aging. The major metric in modeling the performance of a face recognition system is its accuracy of recognition. This paper proposes a novel method which improves the recognition accuracy as well as avoids face datasets being tampered through image splicing techniques. Proposed method uses a non-statistical procedure which avoids training step for face samples thereby avoiding generalizability problem which is caused due to statistical learning procedure. This proposed method performs well with images with partial occlusion and images with lighting variations as the local patch of the face is divided into several different patches. The performance improvement is shown considerably high in terms of recognition rate and storage space by storing train images in compressed domain and selecting significant features from superset of feature vectors for actual recognition.

Ensemble Learning based Age Invariant Fea-ture Recognition Using Soft Computing

2021 ◽  
pp. 7278-7290
Author(s):  
Divyanshu Sinha, Dr J. P. Pandey, Dr. Bhavesh Chauhan
Keyword(s):  
Support Vector Machine ◽  
Face Recognition ◽  
Recognition System ◽  
Support Vector ◽  
Matlab Simulation ◽  
Automated Recognition ◽  
Face Recognition System ◽  
Proposed Model ◽  
Overall Performance ◽  
Vision Application

Face recognition system is a state-of-the-art computer vision application within the artificial intelligence arena. Face recognition is the automated recognition of humans for their names/unique ID. The age invariant face recognition is a challenge task in the field of face recog-nition. In this work, we have introduced a stacked support vector machine where kernel activation of prototype examples is combined in nonlinear ways. The proposed work integrates soft compu-ting-based support vector machine (SVM) with deep SVM. The proposed model uses the implied relation between the variables described above in order to optimize their overall performance. Specifically, our method uses three different stages of complex convolution neural networks that detect and analyze the location of faces position and landmarks. This work has introduced cross-age celebrity dataset (CACD) for both single as well as cross-database enabling the transition of age. The proposed work has been implemented in the MATLAB simulation tool considering CACD dataset. Experimental results indicate that our techniques significantly outperform other strategies across a range of challenging metrics.

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