Thermal Face Recognition Using Moments Invariants

Zaeri, Naser; Baker, Faris; Dib, Rabie

doi:10.12720/ijsps.3.2.94-99

Cited by 5 publications

(6 citation statements)

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“…They elaborated the appropriate moment invariant features selection technique. In [25], the first technique recently introduced for face recognition using the different moment invariant features. The method was investigated on Infrared face images.…”

Section: B Moment Invariant Based Methodsmentioning

confidence: 99%

Novel Face Recognition Framework for Plastic Surgery and Unconstrained Facial Datasets

Ranjanikar*,

Kulkarni

2019

IJEAT

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Since from the last decade, the importance of efficient and robust face recognition becomes interesting research problem due to its growing demand in user authentication process across different applications. Several face recognition method designed by considering the challenges of constrained and unconstrained face acquisition. The performance of face recognition systems becomes worst while working with unconstrained and plastic surgery datasets. The unconstrained dataset may contain the significant variations in illumination, expressions, and face image quality. This paper proposed the novel face recognition framework which is built on top of hybrid face descriptor and hybrid features extraction technique. The purpose of hybrid face descriptor is to achieve the bridge the gap between spatial information and histogram representations to address the challenges of unconstrained face conditions efficiently. The hybrid features extraction includes the histogram features (single and multi-level) and invariant features. Since the moment invariant features become effective shape descriptor to address the challenges of unconstrained face images, we proposed 11 different invariant moments from the hybrid face descriptor. To normalize and reduce the features, we applied lightweight features selection technique. The experimental result shows that proposed face recognition framework improves the overall accuracy different datasets

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Section: B Moment Invariant Based Methodsmentioning

confidence: 99%

Novel Face Recognition Framework for Plastic Surgery and Unconstrained Facial Datasets

Ranjanikar*,

Kulkarni

2019

IJEAT

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“…Here, we should note that the given pictures show for different lightning conditions with the same subject and position, while the actual face temperatures matrices are not exactly the same, although through the pictures cannot be identified. The AOU dataset (Zaeri et al, 2015) includes males and females from a diverse ethnic background using Infrared Camera ETIP 7320.…”

Section: Datasetmentioning

confidence: 99%

Thermal Face Authentication with Convolutional Neural Network

Sayed¹,

Baker²

2018

Journal of Computer Science

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Matching thermal face images as a method of biometric authentication has gained increasing interest because of its advantage of tracking a target object at night and in total darkness. Therefore, for security purposes, it has become highly favourable and has extensive applications, for instance, in video surveillance at night. The aim of this study is to present a simple and efficient deep learning model, which accurately predicts person identification. A pre-trained Convolutional Neural Network (CNN) is employed to extract the features of the multiple convolution layers of the low resolutions' thermal infrared images. To run the program and evaluate the performance, we use a sample of 1500 resized thermal images, each with resolution 181×161 pixels. The sample comprises of images that were captured within different time-lapse and with diverse emotions, poses and lighting conditions. The proposed approach is effective compared to the state-of-the-art thermal face recognition algorithms and achieves impressive accuracy of 99.6% with less processing and training times.

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“…This signature is primarily derived from the pattern of the superficial blood vessels existed under the facial skin. The thermal image is unique for each person since the vein and tissue structure of each face are unique [3].…”

Section: Optimized Superpixel and Adaboost Classifier For Human Thermal Face Recognitionmentioning

confidence: 99%