scholarly journals Face recognition methods analysis

2018 ◽  
Vol 1 (1) ◽  
pp. 01 ◽  
Author(s):  
Shima Zarei
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Component Analysis ◽  
Support Vector ◽  
Data Set ◽  
Linear Discriminant ◽  
Advantages And Disadvantages ◽  
Pattern Histogram ◽  
Color Map ◽  
Rainbow Color

Face Recognition is one of the most important issues in Image processing tasks. It is important because it uses for various purposes in real world such as Criminal detection or for detecting fraud in passport and visa check in airports. Face book is a nice example of Face recognition application, when it sends notification to one user’s friends who are recognized by their images that user uploaded in face book page. To solve Face Recognition problem different methods are introduced such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Hidden Markov Models (HMM) which are explained and analyzed. Also algorithms like; Eigen face, Fisher face and Local Binary Pattern Histogram (LBPH) which are simplest and most accurate methods are implemented in this project for AT&T dataset to recognize the most similar face to other faces in this data set. To this end these algorithms are explained and advantages and disadvantages of each one are analyzed as well. Consequently, the best method is selected with comparison between the results of face reconstruction by Engine face, Fisher face and Local binary pattern histogram methods. In this project Eigen face method has best result. It should be noted that for implementing face recognition algorithms color map methods are used to distinguish the facial features more precisely. In this work Rainbow color map in Eigen Face algorithm and HSV color map in Fisher Face algorithm are utilized and results shows that HSV color map is more accurate than rainbow color map.

scholarly journals Penerapan Face Recognition Pada Sistem Presensi

2021 ◽  
Vol 2 (1) ◽  
pp. 12-17
Author(s):  
Andri Nugraha Ramdhon ◽  
Fadly Febriya
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Monitoring Systems ◽  
Input Process ◽  
Student Attendance ◽  
Human Errors ◽  
Advantages And Disadvantages ◽  
Presence Data ◽  
Pattern Histogram ◽  
Detection And Recognition

The development of digital image technology is increasing nowadays. However, the use of image technology on surveillance cameras has not been optimally utilized. On the other hand, the various presence data monitoring systems that currently exist have their respective advantages and disadvantages, and need to be continuously developed so as to facilitate the data processing. The student attendance system at STT Bandung is basically good but it is still not optimal. The process of collecting student attendance data is still quite time-consuming and still allows human errors to occur in the data input process. Therefore, the author intends to help overcome this by utilizing face recognition technology in an integrated presence process. LBPH (Local Binary Pattern Histogram) is currently the best method in face recognition technology because the detection and recognition process is relatively fast and has the highest level of accuracy when compared to other methods. After testing the resilience of the system from the results of the prototyping that was built, the results obtained with a success rate of 86.85%.

A HYBRID TWO-PHASE ALGORITHM FOR FACE RECOGNITION

2004 ◽  
Vol 18 (08) ◽  
pp. 1423-1435 ◽  
Author(s):  
FRANK Y. SHIH ◽  
KAI ZHANG ◽  
YAN-YU FU
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Second Phase ◽  
Two Phase ◽  
Data Set ◽  
Linear Discriminant ◽  
Database Size

Scientists have developed numerous classifiers in the pattern recognition field, because applying a single classifier is not very conducive to achieve a high recognition rate on face databases. Problems occur when the images of the same person are classified as one class, while they are in fact different in poses, expressions, or lighting conditions. In this paper, we present a hybrid, two-phase face recognition algorithm to achieve high recognition rates on the FERET data set. The first phase is to compress the large class number database size, whereas the second phase is to perform the decision-making. We investigate a variety of combinations of the feature extraction and pattern classification methods. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are examined and tested using 700 facial images of different poses from FERET database. Experimental results show that the two combinations, LDA+LDA and LDA+SVM, outperform the other types of combinations. Meanwhile, when classifiers are considered in the two-phase face recognition, it is better to adopt the L1 distance in the first phase and the class mean in the second phase.

scholarly journals Pengenalan Wajah Manusia berbasis Algoritma Local Binary Pattern

2017 ◽  
Vol 17 (2) ◽  
pp. 29-38
Author(s):  
Ratih Purwati ◽  
Gunawan Ariyanto
Keyword(s):  
Computer Vision ◽  
Support Vector Machine ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Cross Validation ◽  
Support Vector ◽  
Fold Cross Validation

Face Recognition merupakan teknologi komputer untuk mengidentifikasi wajah manusia melalui gambar digital yang tersimpan di database. Wajah manusia dapat berubah bentuk sesuai dengan ekspresi yang dimilikinya. Wajah manusia dapat berubah bentuk sesuai dengan eskpresi yang dimilikinya. Ekspresi wajah manusia memiliki kemiripan satu sama lain sehingga untuk mengenali suatu ekspresi adalah kepunyaan siapa akan sedikit sulit. Pengenalan wajah terus menjadi topik aktif di zaman sekarang pada penelitian bidang computer vision. Penggunaan wajah manusia sering kita jumpai pada fitur-fitur aplikasi media sosial seperti Snapchat, Snapgram dari Instagram dan banyak aplikasi sosial media lainnya yang menggunakan teknologi tersebut. Pada penelitian ini dilakukan analisa pengenalan ekpresi wajah manusia dengan pendekatan fitur alogaritma Local Binary Pattern dan mencari pengembangan alogaritma dasar Local Binary Pattern yang paling optimal dengan cara menggabungkan metode Hisogram Equalization, Support Vector Machine, dan K-fold cross validation sehingga dapat meningkatkan pengenalan gambar wajah manusia pada hasil yang terbaik. Penelitian ini menginput beberapa database wajah manusia seperti JAFFE yang merupakan gambar wajah manusia wanita jepang yang berjumlah 10 orang dengan 7 ekspresi emosional seperti marah, sedih, bahagia, jijik, kaget, takut dan netral ke dalam sistem. YALE yaitu merupakan gambar wajah manusia orang Amerika. Serta menggunakan dataset CALTECH yang merupakan gambar manusia yang terdiri dari 450 gambar dengan ukuran 896 x 592 piksel dan disimpan dalam format JPEG. Kemudian data tersebut di sesuaikan dengan bentuk tekstur wajah masing-masing. Dari hasil penggabungan ketiga metode diatas dan percobaan-percobaan yang sudah dilakukan, didapatkan hasil yang paling optimal dalam pengenalan wajah manusia yaitu menggunakan dataset JAFFE dengan resolusi 92 x 112 piksel dan dengan tingkat penggunaan processor yang tinggi dapat mempengaruhi waktu kecepatan komputasi dalam proses menjalankan sistem sehingga menghasilkan prediksi yang lebih tepat.

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 37-41
Author(s):  
Zhixian Chen ◽  
Jialin Tang ◽  
Xueyuan Gong ◽  
Qinglang Su
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Principal Component ◽  
Activation Function ◽  
Support Vector ◽  
Data Set ◽  
Novel Approach ◽  
Rectified Linear Unit ◽  
The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

scholarly journals Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data

Signals ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 188-208
Author(s):  
Mert Sevil ◽  
Mudassir Rashid ◽  
Mohammad Reza Askari ◽  
Zacharie Maloney ◽  
Iman Hajizadeh ◽  
...  
Keyword(s):  
Physical Activity ◽  
Signal Processing ◽  
Feature Extraction ◽  
Psychological Stress ◽  
Training Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Linear Discriminant ◽  
Physiological Variables

Wearable devices continuously measure multiple physiological variables to inform users of health and behavior indicators. The computed health indicators must rely on informative signals obtained by processing the raw physiological variables with powerful noise- and artifacts-filtering algorithms. In this study, we aimed to elucidate the effects of signal processing techniques on the accuracy of detecting and discriminating physical activity (PA) and acute psychological stress (APS) using physiological measurements (blood volume pulse, heart rate, skin temperature, galvanic skin response, and accelerometer) collected from a wristband. Data from 207 experiments involving 24 subjects were used to develop signal processing, feature extraction, and machine learning (ML) algorithms that can detect and discriminate PA and APS when they occur individually or concurrently, classify different types of PA and APS, and estimate energy expenditure (EE). Training data were used to generate feature variables from the physiological variables and develop ML models (naïve Bayes, decision tree, k-nearest neighbor, linear discriminant, ensemble learning, and support vector machine). Results from an independent labeled testing data set demonstrate that PA was detected and classified with an accuracy of 99.3%, and APS was detected and classified with an accuracy of 92.7%, whereas the simultaneous occurrences of both PA and APS were detected and classified with an accuracy of 89.9% (relative to actual class labels), and EE was estimated with a low mean absolute error of 0.02 metabolic equivalent of task (MET).The data filtering and adaptive noise cancellation techniques used to mitigate the effects of noise and artifacts on the classification results increased the detection and discrimination accuracy by 0.7% and 3.0% for PA and APS, respectively, and by 18% for EE estimation. The results demonstrate the physiological measurements from wristband devices are susceptible to noise and artifacts, and elucidate the effects of signal processing and feature extraction on the accuracy of detection, classification, and estimation of PA and APS.

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