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.

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 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.

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 Weighted Module Linear Regression Classifications for Partially-Occluded Face Recognition

2021 ◽  
Author(s):  
Wei-Jong Yang ◽  
Cheng-Yu Lo ◽  
Pau-Choo Chung ◽  
Jar Ferr Yang
Keyword(s):  
Face Recognition ◽  
Linear Regression ◽  
Recognition Performance ◽  
Recognition System ◽  
Face Image ◽  
Fast Recognition ◽  
The Face ◽  
Robust Face Recognition ◽  
Partially Occluded ◽  
Linear Regression Classification

Face images with partially-occluded areas create huge deteriorated problems for face recognition systems. Linear regression classification (LRC) is a simple and powerful approach for face recognition, of course, it cannot perform well under occlusion situations as well. By segmenting the face image into small subfaces, called modules, the LRC system could achieve some improvements by selecting the best non-occluded module for face classification. However, the recognition performance will be deteriorated due to the usage of the module, a small portion of the face image. We could further enhance the performance if we can properly identify the occluded modules and utilize all the non-occluded modules as many as possible. In this chapter, we first analyze the texture histogram (TH) of the module and then use the HT difference to measure its occlusion tendency. Thus, based on TH difference, we suggest a general concept of the weighted module face recognition to solve the occlusion problem. Thus, the weighted module linear regression classification method, called WMLRC-TH, is proposed for partially-occluded fact recognition. To evaluate the performances, the proposed WMLRC-TH method, which is tested on AR and FRGC2.0 face databases with several synthesized occlusions, is compared to the well-known face recognition methods and other robust face recognition methods. Experimental results show that the proposed method achieves the best performance for recognize occluded faces. Due to its simplicity in both training and testing phases, a face recognition system based on the WMLRC-TH method is realized on Android phones for fast recognition of occluded faces.

3D Face Recognition using an Adaptive Non-Uniform Face Mesh

2011 ◽  
pp. 562-573
Author(s):  
Wei Jen Chew ◽  
Kah Phooi Seng ◽  
Li-Minn Ang
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Face Image ◽  
Uniform Mesh ◽  
Range Image ◽  
3D Face Recognition ◽  
Linear Discriminant ◽  
3D Face ◽  
The Face

Face recognition using 3D faces has become widely popular in the last few years due to its ability to overcome recognition problems encountered by 2D images. An important aspect to a 3D face recognition system is how to represent the 3D face image. In this chapter, it is proposed that the 3D face image be represented using adaptive non-uniform meshes which conform to the original range image. Basically, the range image is converted to meshes using the plane fitting method. Instead of using a mesh with uniform sized triangles, an adaptive non-uniform mesh was used instead to reduce the amount of points needed to represent the face. This is because some parts of the face have more contours than others, hence requires a finer mesh. The mesh created is then used for face recognition purposes, using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Simulation results show that an adaptive non-uniform mesh is able to produce almost similar recognition rates compared to uniform meshes but with significant reduction in number of vertices.

Histogram of Oriented Directional Features for Robust Face Recognition

2016 ◽  
Vol 4 (3) ◽  
pp. 35-51
Author(s):  
Almabrok Essa ◽  
Vijayan K. Asari
Keyword(s):  
Face Recognition ◽  
Feature Vector ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Face Image ◽  
Main Role ◽  
K Nearest Neighbors ◽  
Gaussian Derivative ◽  
The Face ◽  
Robust Face Recognition

This paper presents an illumination invariant face recognition system that uses directional features and modular histogram. The proposed Histogram of Oriented Directional Features (HODF) produces multi-region histograms for each face image, then concatenates these histograms to form the final feature vector. This feature vector is used to recognize the face image by the help of k nearest neighbors classifier (KNN). The edge responses and the relationship among pixels are very important and play the main role for improving the face recognition accuracy. Therefore, this work presents the effectiveness of using different directional masks for detecting the edge responses on face recognition accuracy, such as Prewitt kernels, Kirsch masks, Sobel kernels, and Gaussian derivative masks. The performance evaluation of the proposed HODF algorithm is conducted on several publicly available databases and observed promising recognition rates.

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 Implementasi Algoritma Hard Melokalisasi Fitur Wajah Pada Citra Warna

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
Ujang Juhardi
Keyword(s):  
Face Recognition ◽  
Data Collection ◽  
Face Detection ◽  
Detection System ◽  
Early Stage ◽  
Recognition System ◽  
Image Resolution ◽  
Level Of Detail ◽  
Facial Features ◽  
The Face

AbstrakPendeteksian wajah (face detection) adalah salah satu tahap awal yang sangat penting dalam sistem pengenalan wajah (face recognition) yang digunakan dalam identifikasi biometrik. Sejauh ini, kendala utama yang dihadapi dalam sistem pendeteksian wajah berkisar pada masalah ukuran (resolusi citra), Resolusi citra merupakan tingkat detailnya suatu citra. Semakin tinggi resolusinya semakin tinggi pula tingkat detail dari citra tersebut. Haar like Feature merupakan metode yang lazim digunakan dalam pendeteksian obyek khususnya pendeteksian wajah dan fitur-fiturnya. Prinsip Haar-like features adalah mengenali obyek berdasarkan nilai sederhana dari fitur tetapi bukan merupakan nilai piksel dari image obyek tersebut. Metode ini memiliki kelebihan yaitu komputasinya sangat cepat, karena hanya bergantung pada jumlah piksel dalam persegi bukan setiap nilai piksel dari sebuah image. Untuk mengimplementasikan dan menganalisis kecepatan hasil algoritma haar dalam melokalisasikan fitur wajah penelitian ini menggunakan software MATLAB R2012b agar dapat mengetahui bagaimana cara menganalis dan mengimplementasikan serta mendapatkan hasil menganalisis pengaruh resolusi citra dari algoritma haar dalam melokalisasikan fitur wajah(mata,hidung, dan mulut). Penelitian ini dilaksanakan secara mandiri baik pengambilan data skunder maupun proses pengolahan datanya, untuk metode pengumpulan data pada penelitian ini penulis menggunakan metode studi pustaka dan studi laboratorium. Disarankan dengan adanya penelitian ini, penulis berharap dapat memberikan kontribusi kepada peneliti yang lain untuk meneliti pengaruh-pengaruh lain yang mempengaruhi keberhasilan algoritma haar, sehingga algoritma haar dapat dikembangkan lebih baik lagi.Kata kunci: Pendektesian wajah,resolusi citra, haar like featureAbstractFace detection is one of the early stage is very important in a facial recognition system (face recognition) used in biometric identification. So far, the main obstacle in the face detection system revolves around the issue size (image resolution), the image resolution is the level of detail of an image. The higher the resolution the higher the level of detail of that image .Haar like Feature is a method commonly used in the detection of objects particularly the face detection and its features. Principle Haar-like features are simple to recognize objects based on the value of the feature but not the pixel values of the image of that object. This method has the advantage that the computation is very fast, because it depends on the number of pixels in a square instead of each pixel value of an image. To implement and analyze speed haar algorithm results in a localized facial features of this research using MATLAB R2012b software in order to know how to analyze and implement and get the results to analyze the effect of image resolution algorithm localizes haar in the facial features (eyes, nose, and mouth). This research was carried out independently both secondary data collection and processing of data, for the data collection method in this study the authors use the method of literature and laboratory studies. Suggested the presence of this study, the authors hope to contribute to fellow-researcher to examine other influences which affect the success haar algorithms, so the algorithm can be developed haar better.Keywords: face detection, image resolution, haar like feature

Face recognition in an intelligent door lock with ResNet model based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Low Cost ◽  
Ease Of Use ◽  
Raspberry Pi ◽  
Training Methods ◽  
Graphics Recognition ◽  
Face Images ◽  
The Face ◽  
Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

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