Deep hypersphere embedding for real-time face recognition

Alimuin, Ryann; Dadios, Elmer P.; Dayao, Jonathan; Arenas, Shearyl U.

doi:10.12928/telkomnika.v18i3.14787

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…We based our system on face recognition and detection techniques. There are many techniques for face recognition and detection, for example, local binary patterns (LBP) [8,9], principal component analysis (PCA) [10,11], a combination of PCA, wavelet, and support vector machines (SVM) [12], local binary pattern histogram (LBPH) [13], independent component analysis (ICA) [14,15], eigenfaces [16], and linear discriminant analysis (LDA) [17,18], SVM [19,20], combining fast discrete curvelet transform (FDCvT) and invariant moments with SVM and deep learning technology [21,22]. Dharpure et al [23] proposed a system that utilized counting objects techniques for a fast template matching process based on the normalized cross-correlation (NCC) algorithm.…”

Section: Related Workmentioning

confidence: 99%

ASSAS: An automatic smart students attendance system based on normalized cross-correlation

2021

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A smart student attendance system (SSAS) is presented in this paper. The system is divided into two phases: hardware and software. The Hardware phase is implemented based on Arduino's camera while the software phase is achieved by using image processing with face recognition depended on the cross-correlation technique. In comparison with traditional attendance systems, roll call, and sign-in sheet, the proposed system is faster and more reliable (because there is no action needed by a human being who by its nature makes mistakes). At the same time, it is cheaper when compared with other automatic attendance systems. The proposed system provides a faster, cheaper and reachable system for an automatic smart student attendance that monitors and generates attendance report automatically.

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Section: Related Workmentioning

confidence: 99%

ASSAS: An automatic smart students attendance system based on normalized cross-correlation

2021

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“…Biometric recognition is the approach that comprises most of the identification features [1]. Biometrics can be either a physiological trait such as iris [2,3], face [4][5][6], palm [7,8], ear [9], finger texture [10][11][12], footprint [13] and fingerprint [14][15][16], or a behavioural trait as signature [17], handwriting [18], gait [19,20] and voice [21,22]. On the other hand, traditional methods for individuals' recognition and authentication are based on what a person have or know such as cards, keys, PIN codes, passwords.…”

Section: Introductionmentioning

confidence: 99%

Deep fingerprint classification network

2021

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Fingerprint is one of the most well-known biometrics that has been used for personal recognition. However, faked fingerprints have become the major enemy where they threat the security of this biometric. This paper proposes an efficient deep fingerprint classification network (DFCN) model to achieve accurate performances of classifying between real and fake fingerprints. This model has extensively evaluated or examined parameters. Total of 512 images from the ATVS-FFp_DB dataset are employed. The proposed DFCN achieved high classification performance of 99.22%, where fingerprint images are successfully classified into their two categories. Moreover, comparisons with state-of-art approaches are provided.

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“…Yet, the system is not working accurately with memory (RAM) less than 8 GB. Ryann Alimuin et al, 2020 [16] proposed a robotic security system based on FaceNet. The database consists of 200 sets (12x12 pixels) for each person, in addition to 10 images as test images.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Clustering with a minimum Pattern of Features Extraction for Person Recognition

Safi

Abbas

lbrahim

2021

DJES

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In personal image recognition algorithms, two effective factors govern the system’s evaluation, recognition rate and size of the database. Unfortunately, the recognition rate proportional to the increase in training sets. Consequently, that increases the processing time and memory limitation problems. This paper’s main goal was to present a robust algorithm with minimum data sets and a high recognition rate. Images for ten persons were chosen as a database, nine images for each individual as the full version of the training data set, and one image for each person out of the training set as a test pattern before the database reduction procedure. The proposed algorithm integrates Principal Component Analysis (PCA) as a feature extraction technique with the minimum means of clusters and Euclidean Distance to achieve personal recognition. After indexing the training set for each person, the clustering of the differences is determined. The recognition of the person represented by the minimum mean index; this process returned with each reduction. The experimental results show that the recognition rate is 100% despite reducing the training sets to 44%, while the recognition rate decrease to 70% when the reduction reaches 89%. The clear picture out is the results of the proposed system support the idea of the redaction of training sets in addition to obtaining a high recognition rate based on application requirements.

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Deep hypersphere embedding for real-time face recognition

Cited by 9 publications

References 9 publications

ASSAS: An automatic smart students attendance system based on normalized cross-correlation

ASSAS: An automatic smart students attendance system based on normalized cross-correlation

Deep fingerprint classification network

The Effect of Clustering with a minimum Pattern of Features Extraction for Person Recognition

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