Face Recognition using Deep Learning and TensorFlow framework

Detecting human faces and recognizing faces and facial expressions have always been an area of interest for different applications such as games, utilities and even security. With the advancement of machine learning, the techniques of detection and recognition have become more accurate and precise than ever before. However, machine learning remains a relatively complex field that could feel intimidating or inaccessible to many of us. Luckily, in the last couple of years, several organizations and open-source communities have been developing tools and libraries that help abstract the complex mathematical algorithms in order to encourage developers to easily create learning models and train them using any programming languages. As part of this project, we will create a Face Detection framework in Python built on top of the work of several open-source projects and models with the hope to reduce the entry barrier for developers and to encourage them to focus more on developing innovative applications that make use of face detection and recognition

Understanding critical factors in gender recognition

Gender classification is a task of paramount importance in face recognition research, and it is potentially useful in a large set of applications. In this paper we investigate the gender classification problem by an extended empirical analysis on the Face Recognition Grand Challenge version 2.0 dataset (FRGC2.0). We propose challenging experimental protocols over the dimensions of FRGC2.0 – i.e., subject, face expression, race, controlled or uncontrolled environment. We evaluate our protocols with respect to several classification algorithms, and processing different types of features, like Gabor and LBP. Our results show that gender classification is independent from factors like the race of the subject, face expressions, and variations of controlled illumination conditions. We also report that Gabor features seem to be more robust than LBPs in the case of uncontrolled environment

New Face Representation Using Compressive Sensing

In this paper we present a new descriptor for representing face images. We used compressive sensing concept to prepare a Gaussian Random or Binary Random Measurement Matrix (GRMM). We simply project the face images to new space using GRMM. Classification is then performed using nearest neighbor classifiers. System performance is very promising and comparable with the well-known algorithms in the literature

Face Recognition using SOM Network

This paper presents novel technique for recognizing faces. From the last two decades, face recognition is playing an important and vital role especially in the field of commercial, banking, social and law enforcement area. It is an interesting application of pattern recognition and hence received significant attention. The complete process of face recognition covers in three stages, face detection, feature extraction and recognition. Various techniques are then needed for these three stages. Also these techniques vary from various other surrounding factors such as face orientation, expression, lighting and background. The Self-Organizing Map (SOM) Neural Network has been used for training of database and simulation of FR system. In this paper the feature extraction methods discrete wavelet transform (DWT), discrete cosine transform (DCT) simulated in MATLAB are explained

Texture analysis using volume-radius fractal dimension

Texture plays an important role in computer vision. It is one of the most important visual attributes used in image analysis, once it provides information about pixel organization at different regions of the image. This paper presents a novel approach for texture characterization, based on complexity analysis. The proposed approach expands the idea of the Mass-radius fractal dimension, a method originally developed for shape analysis, to a set of coordinates in 3D-space that represents the texture under analysis in a signature able to characterize efficiently different texture classes in terms of complexity. An experiment using images from the Brodatz album illustrates the method performance.Comment: 4 pages, 4 figure

A New Robust and Discriminating Method for Face Recognition Based on Correlation Technique and Independent Component Analysis Model

International audienceWe demonstrate a novel technique for face recognition combined the independent component analysis (ICA) model with the optical correlation technique. Our approach relies on the performances of a strongly discriminating optical correlation method along with the robustness of the ICA model. Simulations were performed to illustrate how this algorithm can identify a face with images from the Pointing Head Pose Image Database (PHPID). While maintaining algorithmic simplicity, this approach based on ICA representation significantly increases the true recognition rate compared to that obtained with an all numerical ICA identity recognition method, that we recently developed, and with another based on optical correlation and a standard composite filter

Face Recognition System Based on Gabor Wavelets Transform, Principal Component Analysis and Support Vector Machine

Face Recognition is a well-known image analysis application in the branches of pattern recognition and computer vision. It utilizes the uniqueness of human facial characteristics for personnel identification and verification. For a long time, the recognition of facial expressions by using computer-based applications has been an active area of study to recognize face scheme through a face image database. It is used in a variety of essential fields of modern life such as security systems, criminal identification, video retrieval, passport and credit cards. In general, face recognition process can be summarized in three distinct steps: preprocessing, feature extraction, and classification. At first, histogram equalization and median filter are applied as preprocessing methods. Secondly, Gabor wavelets transform extracts the features of desirable facial characterized by, orientation selectivity, spatial locality, and spatial frequency to keep up the variations caused by the varying of facial expression and illumination. In addition to that, Principal Component Analysis methodology (PCA) is used in dimensionality reduction. At last, Support vector machine (SVM) is applied in classifying the feature of the image according to the classis of every mage. In order to test the approach used in this research, experiments were running on Yale database of 165 images from 15 individuals in MATLAB environment. The results obtained from the experiments confirmed the accuracy and robustness of the proposed system

REAL TIME PCA BASED FACE RECOGNITION FOR FOLLOWING STAFF

REAL TIME PCA BASED FACE RECOGNITION FOR FOLLOWING STAFFAbstractWith the development of technology, security has entered our lives as an indispensable element. Nowadays, people are now using some methods that increase safety in every system. Biometrics technologies used in the identification of the physical properties of the body (facial, fingerprint and fingerprint) have become a common security detection approach today. Different methods are used for biometric applications. In this study, an application was developed by using PCA (Principal Component Analysis) method in the literature using face recognition algorithm. In this application, a workplace with hundreds of employees is followed by face recognition of the arrival and departure of the staff. After the follow-up, the persons who are late to the job or who are early to the desired time are reported to the management mail.Keywords: Biometry, Image processing, Facial identification, PCA, Personnel tracking.PERSONEL TAKİBİ İÇİN GERÇEK ZAMANLI PCA TABANLI YÜZ TANIMAÖzetTeknolojinin gelişmesiyle ile birlikte güvenlik vazgeçilmez bir unsur olarak hayatımıza girmiştir. Günümüzde insanlar artık her türlü sistemde güvenliği artıran bazı yöntemler kullanılmaktadır. Kişinin fiziksel özelliklerinin (yüz, parmak izi vs.) kimlik tespitinde kullanılan biyometri teknolojileri, günümüzde oldukça sık karşılaşılan güvenlik tespit yaklaşımı olmuştur. Biyometrik uygulamalar için değişik yöntemler kullanılmaktadır. Bu çalışmada litaretürde bulunan PCA(Principal Component Analysis)  yöntemi ile yüz tanıma algoritması kullanılarak bir uygulama geliştirilmiştir. Bu uygulamada yüzlerce personeli olan bir işyerinde personelin işe geliş ve gidişinin yüz tanıma ile takibi yapılmaktadır.  Takip sonrasında işe istenilen zamandan geç gelen veya istenilen zamandan erken çıkan kişiler yönetime mail olarak bildirilmektedir.Anahtar Kelimeler: Biyometri, Görüntü işleme, Yüz tanımlama, PCA, Personel takip.