PPCU Sam: Open-source face recognition framework

Csaba, Botos; Hakkel, Tamás; Horváth, András; Oláh, András; Reguly, István Z.

doi:10.1016/j.procs.2019.09.367

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Improved facial recognition algorithms and their application in attendance management systems were investigated by Bah [46]. An open source facial recognition framework was investigated by Csaba [47]. Extraction of Feature Density from Areas Important for Face Recognition was investigated by Vinay [48].…”

Section: Face Recognitionmentioning

confidence: 99%

Vehicle Parking Security System with Face Recognition Detection Based on Eigenface Algorithm

Raharja

Fathansyah

Chamim

2021

Journal of Robotics and Control

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RFID (Radio Frequency Identification) card technology is used for intelligent parking systems. Officers no longer need to record and check manually and repeatedly every vehicle that comes in and out with this system. The system has a weakness, namely if the RFId card is dropped or lost, the person who finds it can use the card. For the purpose of increasing security for users, one of the latest technologies is the use of facial recognition methods. The eigenface algorithm system is an algorithm used for face recognition. The algorithm is used as a training process for the previously inputted employee faces. The results of the training data are stored in a database which is then used as a key to recognize the face of the owner of the motorized vehicle who took the motorcycle. In the trial results of the data application, motorized vehicle owners enter when the facial recognition level is above 70%.

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Section: Face Recognitionmentioning

confidence: 99%

Vehicle Parking Security System with Face Recognition Detection Based on Eigenface Algorithm

Raharja

Fathansyah

Chamim

2021

Journal of Robotics and Control

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“…In some studies, these steps are replaced by applying a single deep neural network (DNN) architecture [2,3]. Facial recognition systems were applied in many computer systems, from real-time face tracking and recognition systems [4] and identification of people in personal smartphone galleries [5] to large-scale classification and security systems [6]. Chang et al [7] proposed a facial recognition algorithm based on a support vector machine (SVM) combined with Visual Geometry Group (VGG) network model for extracting facial features, which not only accurately extracts face features, but also reduces feature dimensions and avoids irrelevant features in the calculation.…”

Section: Face Recognitionmentioning

confidence: 99%

A Method for Human Facial Image Annotation on Low Power Consumption Autonomous Devices

Hachaj

2020

Sensors

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This paper proposes a classifier designed for human facial feature annotation, which is capable of running on relatively cheap, low power consumption autonomous microcomputer systems. An autonomous system is one that depends only on locally available hardware and software—for example, it does not use remote services available through the Internet. The proposed solution, which consists of a Histogram of Oriented Gradients (HOG) face detector and a set of neural networks, has comparable average accuracy and average true positive and true negative ratio to state-of-the-art deep neural network (DNN) architectures. However, contrary to DNNs, it is possible to easily implement the proposed method in a microcomputer with very limited RAM memory and without the use of additional coprocessors. The proposed method was trained and evaluated on a large 200,000 image face data set and compared with results obtained by other researchers. Further evaluation proves that it is possible to perform facial image attribute classification using the proposed algorithm on incoming video data captured by an RGB camera sensor of the microcomputer. The obtained results can be easily reproduced, as both the data set and source code can be downloaded. Developing and evaluating the proposed facial image annotation algorithm and its implementation, which is easily portable between various hardware and operating systems (virtually the same code works both on high-end PCs and microcomputers using the Windows and Linux platforms) and which is dedicated for low power consumption devices without coprocessors, is the main and novel contribution of this research.

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Predicting behavioral competencies automatically from facial expressions in real-time video-recorded interviews

Suen

Hung

2021

J Real-Time Image Proc

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PPCU Sam: Open-source face recognition framework

Cited by 3 publications

References 7 publications

Vehicle Parking Security System with Face Recognition Detection Based on Eigenface Algorithm

Vehicle Parking Security System with Face Recognition Detection Based on Eigenface Algorithm

A Method for Human Facial Image Annotation on Low Power Consumption Autonomous Devices

Predicting behavioral competencies automatically from facial expressions in real-time video-recorded interviews

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