Implementation of face recognition system in virtex II Pro platform

Jammoussi, Ameni Yengui; Ghribi, Sameh Fakhfakh; Masmoudi, Dorra Sallami

doi:10.1109/icscs.2009.5412313

Cited by 7 publications

(7 citation statements)

References 3 publications

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“…They have presented a real-time face recognition system with an image resolution of 32 × 32 pixels only. Reference [10] supports a low-resolution image of 60 × 60 pixels, but a real-time application is not specified. Endluri et al [11]…”

Section: Introductionmentioning

confidence: 99%

An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE

Paul¹,

Aslan²

2021

OPJ

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This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment. The face database in this system can be updated via our custom-built standalone android app and automatic restarting of the training and recognition process with an updated database. Using our proposed algorithm, a real-time face recognition accuracy of 78.40% at 15 px and 98.05% at 45 px have been achieved using the LRD200 database containing 200 images per person. With 100 images per person in the database (LRD100) the achieved accuracies are 60.60% at 15 px and 95% at 45 px respectively. A facial deflection of about 30˚ on either side from the front face showed an average face recognition precision of 72.25% -81.85%. This face recognition system can be employed for law enforcement purposes, where the surveillance camera captures a low-resolution image because of the distance of a person from the camera. It can also be used as a surveillance system in airports, bus stations, etc., to reduce the risk of possible criminal threats.

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Section: Introductionmentioning

confidence: 99%

An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE

Paul¹,

Aslan²

2021

OPJ

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“…The algorithm was implemented on a Virtex-2 FPGA, and this architecture can recognize 1400 faces in an image frame, which makes it suitable for real-time face recognition. In [7] an FPGA-based face recognition architecture is proposed, which performs PCA on wavelet transformed images. The architecture is implemented on a Virtex-2 Pro with 46.79 MHz and capable of 100% classification accuracy with the usage of 40 eigenvectors/face.…”

Section: Introductionmentioning

confidence: 99%

FPGA-based low-cost real-time face recognition

Schäffer

Kincses

Pletl

2017

2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY)

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Nowadays face recognition plays a central role in surveillance, biometrics and security. In this paper a Field-Programmable Gate Array (FPGA) based low-cost real-time architecture for face recognition is presented. The face recognition module receives the detected faces from a video stream and processes the data with the widely used Eigenfaces, also known as the Principal Component Analysis (PCA) algorithm. The architecture is implemented on a low-cost Zynq-Z7010 FPGA. The proposed architecture is a part of a system, that capable of finding faces in a crowd based on a preliminarily defined set of faces. In the future it can be integrated into real-time surveillance systems of frequently crowded places (e.g. airports, bus stations), to sort out the supposed sources of threat, and hereby reduce the risk of possible criminal actions.

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“…Therefore, the amount of processing data becomes huge according to its resolution, and the software based embedded application systems are lack of processing performance. So, a number of attempts have been tried to implement face recognition algorithms in FPGA based hardware [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Several face recognition systems have been developed using Eigen faces [2] and PCA [3,4] on FPGA platforms and its hardware implementation results are reported. However, the attempt to remove illumination effect for more robust face recognition has not been presented [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

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Fully hardwired illumination invariant face recognition engine and FPGA implementation

Kim

Lee

Baek

et al. 2015

2015 International Symposium on Consumer Electronics (ISCE)

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Face recognition system plays an important role in many applications such as surveillance, biometrics and security. In this paper, we present a FPGA implementation of real-time face recognition engine consists of face detection, eye detection and feature extraction/verification module. We apply Self-Quotient Image (SQI) to reduce illumination effect. Haar-AdaBoost trained classifiers are applied for face and eye detection. Linear Discriminant Analysis (LDA) and Discrete Cosine Transform (DCT) are used for face recognition. The complete system is implemented in a Xilinx Virtex-7 device. Experimental result shows that our face recognition system operates over 30 fps at 50MHz frequency for real-time applications.

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Implementation of face recognition system in virtex II Pro platform

Cited by 7 publications

References 3 publications

An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE

An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE

FPGA-based low-cost real-time face recognition

Fully hardwired illumination invariant face recognition engine and FPGA implementation

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