Multiresolution Hybrid Approaches for Automated Face Recognition

2007 ◽  
Author(s):  
P. Nicholl ◽  
D. Bouchaffra ◽  
A. Amira ◽  
R.H. Perrott
Keyword(s):  
Face Recognition ◽  
Hybrid Approaches

Local vs. Global

2014 ◽  
pp. 187-205
Author(s):  
Daniel Riccio ◽  
Andrea Casanova ◽  
Gianni Fenu
Keyword(s):  
Face Recognition ◽  
Real World ◽  
High Potential ◽  
Large Set ◽  
Global Features ◽  
Face Representation ◽  
Hybrid Approaches ◽  
The Face ◽  
Local And Global Features ◽  
Local Operators

Face recognition in real world applications is a very difficult task because of image misalignments, pose and illumination variations, or occlusions. Many researchers in this field have investigated both face representation and classification techniques able to deal with these drawbacks. However, none of them is free from limitations. Early proposed algorithms were generally holistic, in the sense they consider the face object as a whole. Recently, challenging benchmarks demonstrated that they are not adequate to be applied in unconstrained environments, despite of their good performances in more controlled conditions. Therefore, the researchers' attention is now turning on local features that have been demonstrated to be more robust to a large set of non-monotonic distortions. Nevertheless, though local operators partially overcome some drawbacks, they are still opening new questions (e.g., Which criteria should be used to select the most representative features?). This is the reason why, among all the others, hybrid approaches are showing a high potential in terms of recognition accuracy when applied in uncontrolled settings, as they integrate complementary information from both local and global features. This chapter explores local, global, and hybrid approaches.

scholarly journals Face Recognition Systems: A Survey

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 342 ◽  
Author(s):  
Yassin Kortli ◽  
Maher Jridi ◽  
Ayman Al Falou ◽  
Mohamed Atri
Keyword(s):  
Face Recognition ◽  
Autonomous Vehicles ◽  
Detailed Comparison ◽  
Image Features ◽  
Face Image ◽  
Image Description ◽  
Future Directions ◽  
The Past ◽  
Hybrid Approaches ◽  
Recognition Systems

Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.

Face in Person Re-Identification

2014 ◽  
pp. 273-288
Author(s):  
Andrea F. Abate ◽  
Stefano Ricciardi ◽  
Genoveffa Tortora
Keyword(s):  
Face Recognition ◽  
Response Speed ◽  
Identity Verification ◽  
Hybrid Approaches ◽  
The Face ◽  
Recognition Systems ◽  
New Research ◽  
New Occurrences ◽  
Large Corpus

The face represents one of the most diffused and established biometrics for both identity verification and recognition with a large corpus of research focused on advancing the accuracy, the robustness, and the response speed of face recognition systems by means of 2D, 3D, and hybrid approaches. One of the new research lines emerging in this field during the last years is face-based people re-identification, namely the task of recognizing new occurrences of an individual's face once it has been detected and initialized at a given time on the same location or eventually at other locations covered by a network of non-overlapping cameras. In this chapter, the main issues and challenges specifically related to face-based people re-identification are described, and the most promising techniques and results proposed on this topic so far are presented and discussed.

Face Recognition Based on Manifold Learning and SVM Classification of 2D and 3D Geodesic Curves

2011 ◽  
pp. 62-81 ◽  
Author(s):  
Stefano Berretti ◽  
Alberto Del Bimbo ◽  
Pietro Pala ◽  
Francisco Josè Silva Mata
Keyword(s):  
Face Recognition ◽  
Manifold Learning ◽  
Three Dimensional ◽  
Still Images ◽  
3D Face ◽  
Hybrid Approaches ◽  
Manifold Embedding ◽  
The Difference ◽  
The One ◽  
2D And 3D

This chapter has a twofold objective. On the one hand, an original approach based on the computation of radial geodesic distances (RGD) is proposed to represent two-dimensional (2D) face images and three-dimensional (3D) face models for the purpose of face recognition. In 3D, the RGD of a generic point of a 3D face surface is computed as the length of the particular geodesic that connects the point with a reference point along a radial direction. In 2D, the RGD of a face image pixel with respect to a reference pixel accounts for the difference of gray level intensities of the two pixels and the Euclidean distance between them. The main contribution of this solution is to permit direct comparison between representations extracted from 2D and 3D facial data, thus opening the way to hybrid approaches for face recognition capable to combine and exploit advantages of different media so as to overcome limitations of traditional solutions based on 2D still images. On the other hand, face representations based on RGDs are used for the purpose of face identification by using them in an operative framework that exploits state of the art techniques for manifold embedding and machine learning. Due to the high dimensionality of face representations based on RGD, embedding into lower-dimensional spaces using manifold learning is applied before classification. Support Vector Machines (SVMs) are used to perform face recognition using 2D- and 3D-RGDs. This shows a general work flow that is not limited to face recognition applications, but can be used in many different contexts of recognition and retrieval. Experimental results are reported for 3D-3D and 2D-3D face recognition using the proposed approach.

Hybrid approaches to frontal view face recognition using the hidden Markov model and neural network

1998 ◽  
Vol 31 (3) ◽  
pp. 283-293 ◽  
Author(s):  
Kang Sik Yoon ◽  
Young Kug Ham ◽  
Rae-Hong Park
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Frontal View ◽  
Hybrid Approaches

scholarly journals PENGGUNAAN MODEL HIDDEN MARKOV DAN METODE NEURAL NETWORK SEBAGAI PENERAPAN TEKNOLOGI PENGENALAN WAJAH

2019 ◽  
Vol 2 (1) ◽  
pp. 13
Author(s):  
Aria Hendrawan ◽  
Basworo Ardi Pramono ◽  
Whisnumurti Adhiwibowo
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition System ◽  
Hybrid Approaches ◽  
Image Characteristics ◽  
Face Recognition System ◽  
The Face ◽  
Sequential Images

The human face recognition system is one of the fields that is quite developed at this time, where applications can be applied in the field of security (security system) such as permission to access room, surveillance (surveillance), as well as the search for individual identities in the police database. The face recognition approach aims to detect faces in 2-dimensional images and sequential images of videos that have many methods such as local, global, and hybrid approaches.  Hidden Model Markov (HMM) is another promising method that works well for images with different lighting variations, facial expressions, and orientations. HMM is a set of statistical models used to characterize signal properties. An artificial neural network-based approach is learned from image examples and relies on techniques from machine learning to find relevant facial image characteristics. The characteristics studied were in the form of discriminant functions (ie non-linear decision surfaces), then used for face recognition. In this study there will be an application to compare Hidden Markov Models and Neural Network Method as a Face Recognition Technology Algorithm Model.  

Face Inversion Effect Emerges Under Critical Configural Discrepancy

2010 ◽  
Vol 69 (3) ◽  
pp. 161-167 ◽  
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.

Perceptual Effects and Recollective Experience in Face Recognition

2005 ◽  
Vol 52 (3) ◽  
pp. 224-231 ◽  
Author(s):  
Chrisanthi Nega
Keyword(s):  
Face Recognition ◽  
Recognition Memory ◽  
Congruency Effect ◽  
Facial Recognition ◽  
Memory Systems ◽  
Study Time ◽  
Perceptual Fluency ◽  
Recollective Experience ◽  
Trace Strength ◽  
Size Congruency

Abstract. Four experiments were conducted investigating the effect of size congruency on facial recognition memory, measured by remember, know and guess responses. Different study times were employed, that is extremely short (300 and 700 ms), short (1,000 ms), and long times (5,000 ms). With the short study time (1,000 ms) size congruency occurred in knowing. With the long study time the effect of size congruency occurred in remembering. These results support the distinctiveness/fluency account of remembering and knowing as well as the memory systems account, since the size congruency effect that occurred in knowing under conditions that facilitated perceptual fluency also occurred independently in remembering under conditions that facilitated elaborative encoding. They do not support the idea that remember and know responses reflect differences in trace strength.

Facing the Facts of Face Recognition: Two Recent Books on the State of Research

1993 ◽  
Vol 38 (1) ◽  
pp. 63-66
Author(s):  
James C. Bartlett
Keyword(s):  
Face Recognition ◽  
The State ◽  
State Of Research
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