scholarly journals Recognition of Augmented Frontal Face Images Using FFT-PCA/SVD Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Francis Ayiah-Mensah ◽  
Louis Asiedu ◽  
Felix O. Mettle ◽  
Richard Minkah
Keyword(s):  
Face Recognition ◽  
Human Brain ◽  
Intelligent Systems ◽  
Numerical Technique ◽  
Recognition System ◽  
Face Image ◽  
Human Beings ◽  
Face Images ◽  
Significant Difference ◽  
The Face

In spite of the differences in visual stimulus of human beings such as ageing, changing conditions of a person, and occlusion, recognition can even be done at a glance by the human eye many years after the previous encounter. It has been established that facial differences like the hairstyle changes, growing of one’s beard, wearing of glasses, and other forms of occlusions can hardly hinder the power of the human brain from making a face recognition. However, the same cannot easily be said about automated intelligent systems which have been developed to mimic the skill of the human brain to aid in recognition. There have been growing interests in developing a resilient and efficient recognition system mainly because of its numerous application areas (access control, entertainment/leisure, security system based on biometric data, and user-friendly human-machine interfaces). Although there have been numerous researches on face recognition under varying pose, illumination, expression, and image degradations, problems caused by occlusions are mostly ignored. This study thus focuses on facial occlusions and proposes an enhancement mechanism through face image augmentation to improve the recognition of occluded face images. This study assessed the performance of Principal Component Analysis with Singular Value Decomposition using Fast Fourier Transform (FFT-PCA/SVD) for preprocessing face recognition algorithm on face images with missingness and augmented face image database. It was found that the average recognition rates for the FFT-PCA/SVD algorithm were the same ( 90 % ) when face images with missingness and augmented face images were used as test images, respectively. The statistical evaluation revealed that there exists a significant difference in the average recognition distances for the face images with missingness and augmented face images when FFT-PCA/SVD is used for recognition. Augmented face images tend to have a relatively lower average recognition distance when used as test images. This finding is contrary to the equal performance assessment by the adopted numerical technique. The MICE algorithm is therefore recommended as a suitable imputation mechanism for enhancing/improving the performance of the face recognition system.

scholarly journals 3D Face Image Inpainting with Generative Adversarial Nets

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Tongxin Wei ◽  
Qingbao Li ◽  
Jinjin Liu ◽  
Ping Zhang ◽  
Zhifeng Chen
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Image Inpainting ◽  
Face Image ◽  
Edge Information ◽  
3D Face ◽  
Face Images ◽  
The Face ◽  
Match Effect ◽  
Traditional Image

In the process of face recognition, face acquisition data is seriously distorted. Many face images collected are blurred or even missing. Faced with so many problems, the traditional image inpainting was based on structure, while the current popular image inpainting method is based on deep convolutional neural network and generative adversarial nets. In this paper, we propose a 3D face image inpainting method based on generative adversarial nets. We identify two parallels of the vector to locate the planer positions. Compared with the previous, the edge information of the missing image is detected, and the edge fuzzy inpainting can achieve better visual match effect. We make the face recognition performance dramatically boost.

The Safety Research for Face Recognition System Based on Image Feature and Digital Watermarking

2012 ◽  
Vol 224 ◽  
pp. 485-488
Author(s):  
Fei Li ◽  
Yuan Yuan Wang
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Face Image ◽  
Image Feature ◽  
Safety Research ◽  
The Face ◽  
Information Image ◽  
Image Integrity ◽  
Recognition Software

Abstract: In order to solve the easily copied problem of images in face recognition software, an algorithm combining the image feature with digital watermark is presented in this paper. As watermark information, image feature of the adjacent blocks are embedded to the face image. And primitive face images are not needed when recovering the watermark. So face image integrity can be well confirmed, and the algorithm can detect whether the face image is the original one and identify whether the face image is attacked by malicious aim-such as tampering, replacing or illegally adding. Experimental results show that the algorithm with good invisibility and excellent robustness has no interference on face recognition rate, and it can position the specific tampered location of human face image.

scholarly journals Unrestricted Face Recognition Algorithm Based on Transfer Learning on Self-Pickup Cabinet

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhixue Liang
Keyword(s):  
Face Recognition ◽  
Transfer Learning ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Face Image ◽  
Delivery Device ◽  
Face Images ◽  
Region Extraction ◽  
The Face ◽  
Enhancement Algorithm

In the contactless delivery scenario, the self-pickup cabinet is an important terminal delivery device, and face recognition is one of the efficient ways to achieve contactless access express delivery. In order to effectively recognize face images under unrestricted environments, an unrestricted face recognition algorithm based on transfer learning is proposed in this study. First, the region extraction network of the faster RCNN algorithm is improved to improve the recognition speed of the algorithm. Then, the first transfer learning is applied between the large ImageNet dataset and the face image dataset under restricted conditions. The second transfer learning is applied between face image under restricted conditions and unrestricted face image datasets. Finally, the unrestricted face image is processed by the image enhancement algorithm to increase its similarity with the restricted face image, so that the second transfer learning can be carried out effectively. Experimental results show that the proposed algorithm has better recognition rate and recognition speed on the CASIA-WebFace dataset, FLW dataset, and MegaFace dataset.

scholarly journals Measurement of Face Detection Accuracy Using Intensity Normalization Method and Homomorphic Filtering

2017 ◽  
Vol 2 (1) ◽  
pp. 107
Author(s):  
I Nyoman Gede Arya Astawa ◽  
I Ketut Gede Darma Putra ◽  
I Made Sudarma ◽  
Rukmi Sari Hartati
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Recognition System ◽  
Face Image ◽  
Normalization Method ◽  
Detection Accuracy ◽  
Normalization Methods ◽  
Homomorphic Filtering ◽  
The Face ◽  
Intensity Normalization

One of the factors that affects the detection system or face recognition is lighting. Image color processing can help the face recognition system in poor lighting conditions. In this study, homomorphic filtering and intensity normalization methods used to help improve the accuracy of face image detection. The experimental results show that the non-uniform of the illumination of the face image can be uniformed using the intensity normalization method with the average value of Peak Signal to Noise Ratio (PSNR) obtained from the whole experiment is 22.05314 and the average Absolute Mean Brightness Error (AMBE) value obtained is 6.147787. The results showed that homomorphic filtering and intensity normalization methods can be used to improve the detection accuracy of a face image.

Face Recognition Algorithm Based on 3D Point Cloud Acquired by Mixed Image Sensor

2019 ◽  
Vol 23 (2) ◽  
pp. 366-369
Author(s):  
Tang-Tang Yi ◽  
Keyword(s):  
Face Recognition ◽  
Point Cloud ◽  
Recognition Accuracy ◽  
Image Sensor ◽  
Recognition Algorithm ◽  
Face Image ◽  
3D Point Cloud ◽  
3D Face ◽  
Face Images ◽  
The Face

In order to solve the problem of low recognition accuracy in recognition of 3D face images collected by traditional sensors, a face recognition algorithm for 3D point cloud collected by mixed image sensors is proposed. The algorithm first uses the 3D wheelbase to expand the face image edge. According to the 3D wheelbase, the noise of extended image is detected, and median filtering is used to eliminate the detected noise. Secondly, the priority of the boundary pixels to recognize the face image in the denoising image recognition process is determined, and the key parts such as the illuminance line are analyzed, so that the recognition of the 3D point cloud face image is completed. Experiments show that the proposed algorithm improves the recognition accuracy of 3D face images, which recognition time is lower than that of the traditional algorithm by about 4 times, and the recognition efficiency is high.

scholarly journals Face Recognition Based on Histogram Equalization and LBP Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 33
Author(s):  
Herman Kh. Omar ◽  
Nada E. Tawfiq
Keyword(s):  
Face Recognition ◽  
Texture Classification ◽  
Histogram Equalization ◽  
Recognition System ◽  
Input Image ◽  
Image Features ◽  
Face Image ◽  
Wide Field ◽  
Face Recognition System ◽  
The Face

In the recent time bioinformatics take wide field in image processing. Face recognition which is basically the task of recognizing a person based on its facial image. It has become very popular in the last two decades, mainly because of the new methods developed and the high quality of the current visual instruments. There are different types of face recognition algorithms, and each method has a different approach to extract the image features and perform the matching with the input image. In this paper the Local Binary Patterns (LBP) was used, which is a particular case of the Texture Spectrum model, and powerful feature for texture classification. The face recognition system consists of recognizing the faces acquisition from a given data base via two phases. The most useful and unique features of the face image are extracted in the feature extraction phase. In the classification the face image is compared with the images from the database. The proposed algorithm for face recognition in this paper adopt the LBP features encode local texture information with default values. Apply histogram equalization and Resize the image into 80x60, divide it to five blocks, then Save every LBP feature as a vector table. Matlab R2019a was used to build the face recognition system. The Results which obtained are accurate and they are 98.8% overall (500 face image).

A Novel Mechanism of Face Recognition Using Stepwise Linear Discriminant Analysis and Linear Vector Quantization Classifiers

2017 ◽  
Vol 7 (7) ◽  
pp. 48
Author(s):  
Abdul Quyoom
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Vector Quantization ◽  
Recognition System ◽  
Face Image ◽  
Linear Discriminant ◽  
Linear Vector ◽  
Face Features ◽  
The Face

Face recognition is a hard and special case of computer vision and pattern recognition. It is a challenging problem due to various kinds of variations of face images.  This paper proposes a robust face recognition system. Here stepwise linear discriminant analysis (SWLDA) is used for the feature extraction and Linear Vector Quantization (LVQ) Classifier is used for face recognition. The main focus of SWLDA is to select localized features from the face. In order to increase the low-between-class variance and to reduce within-class-variance among different expression classes and use F-test value through which results are analyzed. In recognition, firstly face is detected using canny edge detection method, after face detection SWLDA is employed to extract the face features, and end linear vector quantization is applied for face recognition. To achieve optimum results and increase the robustness of the proposed system, experiments are performed on various different samples of face image, which consist of face image with the different pose and facial expression in order to validate the system, we use two famous datasets which include Yale and ORL face database.

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>

ADAPTIVE CLASSIFIER INTEGRATION FOR INVARIANT FACE RECOGNITION (ACIIFR)

2002 ◽  
Vol 16 (06) ◽  
pp. 749-772 ◽  
Author(s):  
G. A. KHUWAJA ◽  
M. S. LAGHARI
Keyword(s):  
Face Recognition ◽  
Network Architecture ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Face Image ◽  
Divide And Conquer ◽  
Accuracy Rate ◽  
Learning Time ◽  
The Face

The integration of multiple classifiers promises higher classification accuracy and robustness than can be obtained with a single classifier. We address two problems: (a) automatic recognition of human faces using a novel fusion approach based on an adaptive LVQ network architecture, and (b) improve the face recognition up to 100% while maintaining the learning time per face image constant, which is an scalability issue. The learning time per face image of the recognition system remains constant irrespective of the data size. The integration of the system incorporates the "divide and conquer" modularity principles, i.e. divide the learning data into small modules, train individual modules separately using compact LVQ model structure and still encompass all the variance, and fuse trained modules to achieve recognition rate nearly 100%. The concept of Merged Classes (MCs) is introduced to enhance the accuracy rate. The proposed integrated architecture has shown its feasibility using a collection of 1130 face images of 158 subjects from three standard databases, ORL, PICS and KU. Empirical results yield an accuracy rate of 100% on the face recognition task for 40 subjects in 0.056 seconds per image. Thus, the system has shown potential to be adopted for real time application domains.

scholarly journals The Face Recognition Method Based on CS-LBP and DBN

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Kun Sun ◽  
Xin Yin ◽  
Mingxin Yang ◽  
Yang Wang ◽  
Jianying Fan
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Texture Feature ◽  
Texture Features ◽  
Face Image ◽  
Recognition Method ◽  
Traditional Methods ◽  
Face Images ◽  
The Face

At present, the face recognition method based on deep belief network (DBN) has advantages of automatically learning the abstract information of face images and being affected slightly by active factors, so it becomes the main method in the face recognition area. Because DBN ignores the local information of face images, the face recognition rate based on DBN is badly affected. To solve this problem, a face recognition method based on center-symmetric local binary pattern (CS-LBP) and DBN (FRMCD) is proposed in this paper. Firstly, the face image is divided into several subblocks. Secondly, CS-LBP is used to extract texture features of each image subblock. Thirdly, texture feature histograms are formed and input into the DBN visual layer. Finally, face classification and face recognition are completed through deep learning in DBN. Through the experiments on face databases ORL, Extend Yale B, and CMU-PIE by the proposed method (FRMCD), the best partitioning way of the face image and the hidden unit number of the DBN hidden layer are obtained. Then, comparative experiments between the FRMCD and traditional methods are performed. The results show that the recognition rate of FRMCD is superior to those of traditional methods; the highest recognition rate is up to 98.82%. When the number of training samples is less, the FRMCD has more significant advantages. Compared with the method based on local binary pattern (LBP) and DBN, the time-consuming of FRMCD is shorter.

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