Face Recognition using Fast Fourier Transform

2016 ◽  
pp. 302-322
Author(s):  
Shivakumar Baragi ◽  
Nalini C. Iyer
Keyword(s):  
Face Recognition ◽  
Skin Color ◽  
Color Image ◽  
Rejection Rate ◽  
Threshold Value ◽  
Recognition System ◽  
Mean Value ◽  
Test Image ◽  
The Matrix ◽  
Test Database

Biometrics refers to metrics related to human characteristics and Traits. Face Recognition is the process of identification of a person by their facial image. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. The objective is to authenticate a person, to have a FAR and FRR very low. This project introduces a new approach for face recognition system using FFT algorithm. The database that contains the images is named as train database and the test image which is stored in test database is compared with the created train database. For further processing RGB data is converted into grayscale, thus reduces the matrix dimension. FFT is applied to the entire database and mean value of the images is computed and the same is repeated on test database also. Based on the threshold value of the test image, face recognition is done. Performance evaluation of Biometrics is done for normal image, skin color image, ageing image and blur image using False Acceptance Rate(FAR), False Rejection Rate(FRR), Equal Error Rate(EER) and also calculated the accuracy of different images.

A Face Recognition System Based on ASM Facial Components

2011 ◽  
Vol 58-60 ◽  
pp. 2314-2319
Author(s):  
Chen Chiung Hsieh ◽  
Wei Hsu Chen
Keyword(s):  
Face Recognition ◽  
Skin Color ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Recognition System ◽  
Facial Features ◽  
Horizontal Line ◽  
Horizontal Projection ◽  
Dynamic Background ◽  
Edge Points

This paper proposed five new types of facial features for face recognition. Ada-boost is used to detect face firstly. False detected faces are removed by dynamic background modeling and skin color detection. Skewed face is also calibrated to achieve higher accuracy. Based on Active Shape Modeling, the five new types of facial features including gradient histograms of facial components, vertical/horizontal projection of facial edge points, signature of facial components, multiple vertical/horizontal line segments within facial shape, and face template could be extracted. According to the classification capability, features are associated with different weights while during matching. Nearest neighbor classifier is deployed for face recognition by using the averaged of feature points of a person as the center. The size of database is 200 people which are selected from the face databases of MIT and ESSEX. Five images per person were used for training and 491 images were tested. The recognition rate was 98.3% and the processing speed reached 220ms per frame on a general personal computer.

Research of Face Recognition System Based on TMS320DM642

2013 ◽  
Vol 341-342 ◽  
pp. 975-979
Author(s):  
Zhen Liu ◽  
Ji Chao Yuan ◽  
Shuai Mei ◽  
Xiong Shi
Keyword(s):  
Face Recognition ◽  
Digital Signal Processor ◽  
Skin Color ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Recognition System ◽  
Detection Algorithm ◽  
Gabor Wavelet ◽  
Face Recognition System ◽  
System Solution

This paper proposes an embedded face recognition system solution. The core of hardware architecture of the system is TMS320DM642 digital signal processor (DSP). The face recognition algorithm of the system mainly comprises improved face detection algorithm which is based on skin color, Gabor wavelet feature extraction algorithm, Principal Component Analysis (PCA) algorithm and nearest neighbor classifier algorithm. The results of testing on recognition efficiency and execution time show that the system can work stably and realize face recognition quickly and accurately.

An efficient object detection using OFSA for thermal imaging

2020 ◽  
pp. 002072092094443
Author(s):  
V Teju ◽  
D Bhavana
Keyword(s):  
Face Recognition ◽  
Object Detection ◽  
Thermal Imaging ◽  
Visual Recognition ◽  
Transfer Functions ◽  
Recognition System ◽  
Visual Images ◽  
Thermal Images ◽  
Security Issues ◽  
The Matrix

The demand for identifying a reliable person is increased because of security issues in our daily life. At present, to identify a person biometric technique such as face recognition is introduced. Since,a person with abnormal behaviour recognition system has reached certain level, their accomplishments in real time applications are restricted by challenges, such as illumination variations. The present visual recognition system is good at controlled illumination conditions and thermal face recognition system is better for detecting disguised persons or when there is no illumination control. Hence, a hybrid system which uses both visual and thermal images for recognising a person is better. The objective of this research is to implement a method which improves the quality of the image by fusing visual and thermal imaging images. Our research methodology has introduced to enhance servo line camera images. Nonlinear image transfer functions were introduced,and the parameters associated with those functions are determined by image statistics for making adaptive algorithms. Next methodswereintroduced for registering the visual images to their consequent thermal images. To get a transformation matrix for the registration, the landmarks in the images are first detected and a subset of those landmarks were selected to obtain the matrix, we propose a hybrid algorithm for detection, tracking and classification using OFSA algorithm to fuse the registered thermal and visual images. In this research, we focus on object detection using OFSA algorithm for more accuracy.

scholarly journals Man in the Middle of Face Recognition System: using Skin Color and Template

2019 ◽  
Vol 9 (2) ◽  
pp. 2601-2610
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Skin Color ◽  
Initial Step ◽  
Recognition System ◽  
Feedback Mechanism ◽  
Query Image ◽  
Face Recognition System ◽  
The Face ◽  
Compensation Technique

During last 10 years people are very much attracted to face recognition systems and they are very much eager to solve the issues related to face recognition system. It helped them very much in the field of electronics and uses over pattern unlocking and password entering system. There are numerous applications as for security, affectability and mystery. Detection of a face is the most significant and initial step of recognition framework. This article demonstrates a new method to face recognition system using color and template of an image. Whatever the background it may go to be, our system will detect the face, which is an important stage for face detection. The pictures utilized in this framework for Face detection are the color images, while the images used for the Face Recognition are the Gray images which are converted from color pictures. The illumination compensation technique is applied on all the images for removing the effect of light. The Red, Green, and Blue values of each pixel will be converted to YCbCr space. Based on the probability of each pixel in terms of Cb, Cr values, we extract the skin pixels from the query image,. The positive probability shows a “skin pixel”, while the negative probability shows “not a skin pixel”. Finally the face is projected. In face recognition, we used 4 templates of different sizes for Gabor image content extraction. Finally we employed the relevance feedback mechanism to retrieve the most similar images. If the user did not satisfy with the given results he can give the correct images to the system from the displayed images. Exploratory outcomes demonstrate that the demonstrated system is adequate to recognize face of a human face in a picture with an exactness of 94%.

scholarly journals Design of face recognition system based on CNN

2020 ◽  
Vol 1601 ◽  
pp. 052011
Author(s):  
Yong Li ◽  
Zhe Wang ◽  
Yang Li ◽  
Xu Zhao ◽  
Hanwen Huang
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Face Recognition System

scholarly journals Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 526
Author(s):  
Yang Han ◽  
Chunbao Liu ◽  
Lingyun Yan ◽  
Lei Ren
Keyword(s):  
Decision Tree ◽  
Time Window ◽  
Threshold Value ◽  
Recognition System ◽  
Tree Structure ◽  
Classification Model ◽  
Measurement Unit ◽  
Local Optima ◽  
Mode Recognition ◽  
Locomotion Mode

Smart wearable robotic system, such as exoskeleton assist device and powered lower limb prostheses can rapidly and accurately realize man–machine interaction through locomotion mode recognition system. However, previous locomotion mode recognition studies usually adopted more sensors for higher accuracy and effective intelligent algorithms to recognize multiple locomotion modes simultaneously. To reduce the burden of sensors on users and recognize more locomotion modes, we design a novel decision tree structure (DTS) based on using an improved backpropagation neural network (IBPNN) as judgment nodes named IBPNN-DTS, after analyzing the experimental locomotion mode data using the original values with a 200-ms time window for a single inertial measurement unit to hierarchically identify nine common locomotion modes (level walking at three kinds of speeds, ramp ascent/descent, stair ascent/descent, Sit, and Stand). In addition, we reduce the number of parameters in the IBPNN for structure optimization and adopted the artificial bee colony (ABC) algorithm to perform global search for initial weight and threshold value to eliminate system uncertainty because randomly generated initial values tend to result in a failure to converge or falling into local optima. Experimental results demonstrate that recognition accuracy of the IBPNN-DTS with ABC optimization (ABC-IBPNN-DTS) was up to 96.71% (97.29% for the IBPNN-DTS). Compared to IBPNN-DTS without optimization, the number of parameters in ABC-IBPNN-DTS shrank by 66% with only a 0.58% reduction in accuracy while the classification model kept high robustness.

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