Landmark localization for face recognition

2015 ◽  
pp. 279-298
Keyword(s):  
Face Recognition ◽  
Landmark Localization

scholarly journals FAN-Face: a Simple Orthogonal Improvement to Deep Face Recognition

2020 ◽  
Vol 34 (07) ◽  
pp. 12621-12628 ◽  
Author(s):  
Jing Yang ◽  
Adrian Bulat ◽  
Georgios Tzimiropoulos
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Simple Approach ◽  
Feature Integration ◽  
Prior Work ◽  
Shape Information ◽  
Facial Landmarks ◽  
Facial Landmark Localization ◽  
Facial Landmark ◽  
Landmark Localization

It is known that facial landmarks provide pose, expression and shape information. In addition, when matching, for example, a profile and/or expressive face to a frontal one, knowledge of these landmarks is useful for establishing correspondence which can help improve recognition. However, in prior work on face recognition, facial landmarks are only used for face cropping in order to remove scale, rotation and translation variations. This paper proposes a simple approach to face recognition which gradually integrates features from different layers of a facial landmark localization network into different layers of the recognition network. To this end, we propose an appropriate feature integration layer which makes the features compatible before integration. We show that such a simple approach systematically improves recognition on the most difficult face recognition datasets, setting a new state-of-the-art on IJB-B, IJB-C and MegaFace datasets.

scholarly journals Generating images of partial face using landmark based k-nearest neighbor

2020 ◽  
Vol 17 (1) ◽  
pp. 420 ◽  
Author(s):  
Israa Hadi ◽  
Alyaa Mahdi
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
Face Image ◽  
K Nearest Neighbor ◽  
Supervised Clustering ◽  
Facial Landmarks ◽  
Key Points ◽  
Full Face ◽  
Suitable Technique ◽  
Landmark Localization

<p>One of the most common approaches to address the partial face recognition challenge is to crop the full face image into segments. The problem is how the full face image must be cropped in a uniform way to generate informative segments. The un-blindly strategy was applied in this paper to generate informative segments, it depends on localizing the facial landmarks and selecting the more informative facial points as a key points, as more as the k-nearest neighbor concept was explored to select the k nearest landmark points to the key points. Two landmark localization techniques were experimented, the suitable technique  resulted in segments which are overlapped due to the supervised clustering technique that explored in this paper to cover important biometric face regions, not repeated and covered most probabilities in which it is possible to distinguish the query face from the available part of it.<strong><em></em></strong></p>

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.

Proclivity to Choose on Face Recognition Predicts PTC on Lineups

2014 ◽  
Author(s):  
Mario Baldassari ◽  
Justin Kantner ◽  
D. Stephen Lindsay
Keyword(s):  
Face Recognition
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