Nonrigid Registration Algorithm in the Face Recognition Problem

The face recognition problem is a topical issue in computer vision. In this paper we propose a customized version of the orthogonalization via deflation algorithm to tackle this problem. We test the new proposed algorithm on two datasets: the well-known ORL dataset and an own face dataset, CTOVF; also, we compare our results (in terms of rate recognition and average quiery time) with the outcome of a standard algorithm in this class (dimension reduction methods using numerical linear algebra tools).

Download Full-text

Performance Analysis of the Feedforward and SOM Neural Networks in the Face Recognition Problem

2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing ◽

10.1109/ciisp.2007.369187 ◽

2007 ◽

Cited By ~ 4

Author(s):

M.I. Chacon M. ◽

P. Rivas-Perea

Keyword(s):

Neural Networks ◽

Face Recognition ◽

Performance Analysis ◽

Recognition Problem ◽

The Face

Download Full-text

Aligned PSO for Optimization of Image Processing Methods Applied to the Face Recognition Problem

Swarm, Evolutionary, and Memetic Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-319-03753-0_57 ◽

2013 ◽

pp. 642-651

Author(s):

Juan Luis Fernández-Martínez ◽

Ana Cernea ◽

Esperanza García-Gonzalo ◽

Julian Velasco ◽

Bijaya Ketan Panigrahi

Keyword(s):

Image Processing ◽

Face Recognition ◽

Recognition Problem ◽

Processing Methods ◽

The Face

Download Full-text

Face Inversion Effect Emerges Under Critical Configural Discrepancy

Swiss Journal of Psychology ◽

10.1024/1421-0185/a000018 ◽

2010 ◽

Vol 69 (3) ◽

pp. 161-167 ◽

Cited By ~ 3

Author(s):

Jisien Yang ◽

Adrian Schwaninger

Keyword(s):

Face Recognition ◽

Face Processing ◽

Underlying Mechanism ◽

Inversion Effect ◽

Configural Processing ◽

Major Contributor ◽

Face Inversion Effect ◽

Configural Information ◽

Face Inversion ◽

The Face

Configural processing has been considered the major contributor to the face inversion effect (FIE) in face recognition. However, most researchers have only obtained the FIE with one specific ratio of configural alteration. It remains unclear whether the ratio of configural alteration itself can mediate the occurrence of the FIE. We aimed to clarify this issue by manipulating the configural information parametrically using six different ratios, ranging from 4% to 24%. Participants were asked to judge whether a pair of faces were entirely identical or different. The paired faces that were to be compared were presented either simultaneously (Experiment 1) or sequentially (Experiment 2). Both experiments revealed that the FIE was observed only when the ratio of configural alteration was in the intermediate range. These results indicate that even though the FIE has been frequently adopted as an index to examine the underlying mechanism of face processing, the emergence of the FIE is not robust with any configural alteration but dependent on the ratio of configural alteration.

Download Full-text

Real Time Person Recognition and Attendance Marking Using Holistic Based Approach

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0220 ◽

2017 ◽

Vol 7 (7) ◽

pp. 302

Author(s):

Reshma P ◽

Muneer VK ◽

Muhammed Ilyas P

Keyword(s):

Face Recognition ◽

Real Time ◽

High Accuracy ◽

Efficient Technique ◽

Human Face ◽

Traditional Methods ◽

Person Recognition ◽

The Face

Face recognition is a challenging task for the researches. It is very useful for personal verification and recognition and also it is very difficult to implement due to all different situation that a human face can be found. This system makes use of the face recognition approach for the computerized attendance marking of students or employees in the room environment without lectures intervention or the employee. This system is very efficient and requires very less maintenance compared to the traditional methods. Among existing methods PCA is the most efficient technique. In this project Holistic based approach is adapted. The system is implemented using MATLAB and provides high accuracy.

Download Full-text

Real Time Gender and Age Prediction using Deep Learning Techniques

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.e2906.049620 ◽

2020 ◽

Vol 9 (6) ◽

pp. 797-801

Keyword(s):

Deep Learning ◽

Face Recognition ◽

Real Time ◽

Vital Role ◽

Learning Techniques ◽

Gender And Age ◽

The Face ◽

And Gender ◽

Gender Detection ◽

Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

Download Full-text

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189624 ◽

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.

Download Full-text

View-tuned and view-invariant face encoding in IT cortex is explained by selected natural image fragments

Scientific Reports ◽

10.1038/s41598-021-86842-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yunjun Nam ◽

Takayuki Sato ◽

Go Uchida ◽

Ekaterina Malakhova ◽

Shimon Ullman ◽

...

Keyword(s):

Face Recognition ◽

Visual Pathway ◽

Visual Features ◽

Natural Image ◽

Face Representation ◽

Low Level ◽

Ventral Visual Pathway ◽

Neural Substrate ◽

The Face

AbstractHumans recognize individual faces regardless of variation in the facial view. The view-tuned face neurons in the inferior temporal (IT) cortex are regarded as the neural substrate for view-invariant face recognition. This study approximated visual features encoded by these neurons as combinations of local orientations and colors, originated from natural image fragments. The resultant features reproduced the preference of these neurons to particular facial views. We also found that faces of one identity were separable from the faces of other identities in a space where each axis represented one of these features. These results suggested that view-invariant face representation was established by combining view sensitive visual features. The face representation with these features suggested that, with respect to view-invariant face representation, the seemingly complex and deeply layered ventral visual pathway can be approximated via a shallow network, comprised of layers of low-level processing for local orientations and colors (V1/V2-level) and the layers which detect particular sets of low-level elements derived from natural image fragments (IT-level).

Download Full-text