Optimal Face Classification by Using Nonsingular Discriminant Waveletfaces for a Face Recognition

Face recognition using sparse representation-based classification (SRC) is a new hot technique in recent years. However, the research indicates that it is the collaborative representation but not the [Formula: see text]-norm sparsity that makes SRC powerful for face classification. Consequently, we propose a simple yet much more efficient face classification scheme, namely two-step collaborative representation-based classification (TSCRC) method. First, we exploit the symmetry of the face to generate new images of each test sample. Then, the original and new generated test samples are, respectively, used to perform TSCRC, which ultimately uses a small number of classes that are near to the test sample to represent and classify it. Finally, the score level fusion is taken to perform classification recognition. The experimental results clearly show that the proposed method has very competitive classification results.

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Face Recognition of Passenger for Bus Services

10.30880/eeee.2021.02.01.007 ◽

2021 ◽

Author(s):

Mohammad Azerul Azlan ◽

◽

Abd Kadir Mahamad ◽

Sharifah Saon ◽

◽

...

Keyword(s):

Face Recognition ◽

Internet Of Things ◽

University Students ◽

Recognition System ◽

Raspberry Pi ◽

Counting System ◽

Face Classification ◽

Face Recognition System ◽

Bus Services

Most university students are using the bus provided by the university's management to move from one place to another place. The analysis are required to improvise the quality of the of bus services such as the amount of passenger that using the bus and information of passengers such as gender. The objectives of this project are to develop face recognition system based on gender using Raspberry Pi 4 and Intel Neural Compute Stick 2 and to test and validate the performance of the developed system for face classification and passenger counting system. Also this system is able to store passenger information into Google Firebase Cloud with Internet of Things. This system is used Raspbian in Raspberry Pi 4 with the libraries that used for face classification and recognition such as OpenCV and OpenVINO. This project able to detect faces of the passengers soon as they ride the bus and determine gender of the passengers and count passengers according gender and the information of the passengers will stored in Google Firebase. There are some recommendation that need to be added in this project to improve efficiency of the system.

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A Gist Warning of Sighting in Face Classification and Recognition

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d7801.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 11166-11177

Keyword(s):

Face Recognition ◽

Real Time ◽

High Speed ◽

Large Range ◽

A Priori ◽

Extraction Techniques ◽

Face Classification ◽

The Face ◽

Real Time Applications ◽

The Moment

Face classification and recognition is the fastest growing, challenging area in real time applications. A large number of algorithms are there in the network to recognize the face. It is the important part of the biometric traits and it not only contributes to the theoretical insights but also to practical insights of many algorithms. Conversely, the first face recognition in the main reckons on a priori in a row of hurdle folks and might not free itself from human intervention. Until the looks of high-speed, betterquality computers, the face recognition methodology makes a big disintegrate through. Face recognition has been a quick growing, difficult and mesmerizing space in real time applications. Facial classifications and recognition becomes an interesting research topic. A large range of face classification and recognition algorithms are developed in last decades. In this paper a attempt is created to review a good vary of strategies used for face recognition expansively. This paper contributes a huge survey of varied face detection and feature extraction techniques. At the moment, there are loads of face classification and recognition techniques and algorithms found and developed round the world.

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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.

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Perceptual Effects and Recollective Experience in Face Recognition

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169.52.3.224 ◽

2005 ◽

Vol 52 (3) ◽

pp. 224-231 ◽

Cited By ~ 7

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.

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