Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Face recognition

Smart Door for Handicapped People via Face Recognition and Voice Command Technique

Hanaa M. Salman; Rana T. Rasheed

Engineering and Technology Journal, 2021, Volume 39, Issue 1B, Pages 222-230
DOI: 10.30684/etj.v39i1B.1719

Smart home indicates an application for different technological implementations, it could indicate any system which controls the door lock and several other devices. Facial identification which is an important section to achieve surveillance and safety, especially for handicapped people, can be considered as one of the ways that deal with biometrics and performed to identify facial images via utilizing fundamental features of the face. A Raspberry Pi-based face recognition system using conventional face detection and recognition techniques is going to be supplied, so the method in which image-built biometrics uses a Raspberry Pi is described. The aim of the paper here can be considered as transferring face recognition to a level in which the system can replace the utilizing of RF I-Cards and a password to access any system of security and making the system alive and protect the door from being open by hackers, especially by using the picture of an authorized person, we make the raspberry pi turn off and cannot turn on only by a command from the authorized person's mobile. The result of the presented proposal is a system that uses face recognition by utilizing OpenCV, Raspberry Pi, and it functions on an application of Android, and this system percentage becomes 99.63%. It should be cost-effective, of high performance, secured, and easy to use, which can be used in any smart home application.

A New Hybrid Technique for Face Identification Based on Facial Parts Moments Descriptors

Shaymaa M. Hamandi; Abdul Monem S. Rahma; Rehab F. Hassan

Engineering and Technology Journal, 2021, Volume 39, Issue 1B, Pages 117-128
DOI: 10.30684/etj.v39i1B.1903

Robust facial feature extraction is an effective and important process for face recognition and identification system. The facial features should be invariant to scaling, translation, illumination and rotation, several feature extraction techniques may be used to increase the recognition accuracy. This paper inspects three-moment invariants techniques and then determines how is influenced by the variation which may happen to the various shapes of the face (globally and locally) Globally means the whole face shapes and locally means face part's shape (right eye, left eye, mouth, and nose). The proposed technique is tested using CARL database images. The proposal method of the new method that collects the robust features of each method is trained by a feed-forward neural network. The result has been improved and achieved an accuracy of 99.29%.

Effect Of Eigenfaces Level On The Face Recognition Rate Using Principal Component Analysis

Eyad. I. Abbas

Engineering and Technology Journal, 2015, Volume 33, Issue 3, Pages 729-737

This paper presents an approach to study the effect of the different eigenfaces levels on the faces recognition rate using principal component analysis. The increase in the strength of the variables and the lighting in the facial geometry to represent the human face , has been using the principal component analysis (PCA) on the image of the whole face . The principal component analysis is a statistical measurement method , which works in the field of linear and can be used to reduce the dimensions of the image and thus serve to reduce the calculations significantly to the image database . It is a method gives better accuracy and a higher rate of recognition . The experiment was conducted on 50 images from the database of faces (ORL), using 40 images for the training set and 15 images for the test group ( five images in common with the training set and the remaining 10 images are different in expression and corner ) . The results proved that the proposed method is effective and successful in obtaining recognition rate up to 100% in the third level when using ten eigenfaces.

Face Recognition Based on PCA, LBP and SVM Techniques

Omar Ibrahim Yehya Dallal Bashi

Engineering and Technology Journal, 2015, Volume 33, Issue 3, Pages 384-392

Although many of methods have accomplished good success in face recognition systems, but most of them are unable to achieve recognition by using a single sample per person. In this paper, a combination of three techniques represented by local binary pattern (LBP), principal component analysis (PCA) and support vector machine (SVM) is used to present face recognition system has the ability to recognize face depending on Single Sample per Person only. The LBP and PCA are applied to extract the important features as well as reduce the dimension of the image face while the SVM is applied to classify these features according to the classes that belong to its. The proposed approach was evaluated on Yale database and the experimental results showed distinct improvement of the proposed method compared with traditional PCA based SVM classifier.

Improvement of Face Recognition System Based on Linear Discrimination Analysis and Support Vector Machine

Thair A. Salh; Mustafa Zuhaer Nayef

Engineering and Technology Journal, 2013, Volume 31, Issue 12, Pages 2261-2272

Face recognition is one of the most important research fields in many of applications
and it is used in various domains including human computer interaction, security
systems and personal identification. Many of face recognition systems have been
developed for decades. In general, the accuracy of the face recognition system is
determined by the accuracy of the method that is used to extract features and the
accurate of the classification method. This paper introduces an improvement of face
recognition system by using Linear Discrimination Analysis and Support Vector
Machine. Two types of experiments off-line and on-line are done. In off-line
experiment, the Olivetti Research Laboratory face database is used and in on-line
experiment, DVD Maker 2 adapter is used to capture live image from digital camera,
and digitalize it to be compared with training database. The Comparison with Linear
Discriminate Analysis and Artificial Neural Network is implemented .The results show
that the proposed method gives better results in off-line experiment than previous
methods in terms of recognition rate.

Face Recognition using DWT with HMM

Eyad I. Abbas; Hameed R. Farhan

Engineering and Technology Journal, 2012, Volume 30, Issue 1, Pages 142-154

This paper presents an efficient face recognition system based on Hidden Markov
Model (HMM) and the simplest type “Haar” of the Discrete Wavelet Transform
(DWT). The one dimensional ergodic HMM with Gaussian outputs, which represent
the simplest and robust type of HMM, is used in the proposed work. A novel method
is introduced for selecting the training images implemented by choosing the images
that have the odd identifying numbers from the database. Some of these images are
replaced according to the trial-and-error results. The proposed work achieves the
maximum recognition rate (100%), where the experiments are carried out on the ORL
face database.