2D Pose-Invariant Face Recognition Using Single Frontal-View Face Database

Petpairote, Chayanut; Madarasmi, Suthep; Chamnongthai, Kosin

doi:10.1007/s11277-020-07063-1

Cited by 11 publications

(13 citation statements)

References 32 publications

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“…With advancement in digital technology, face recognition is also being used in various cyber investigations [ 1 , 2 ]. This complements well with the aftermath of the COVID-19 outbreak, which has forced the world to adopt face recognition technology with a primary focus on the contact-less operation [ 3 , 4 ]. The most prominent issue that affects the outcome of face recognition systems is related to the illumination variation, which may be due to varying lighting conditions [ 5 ].…”

Section: Introductionmentioning

confidence: 66%

“…The most prominent issue that affects the outcome of face recognition systems is related to the illumination variation, which may be due to varying lighting conditions [ 5 ]. Aside from this, concerns with posture variation or camera angles can cause significant changes in facial appearance and/or form, as well as intra-subject face variations [ 2 , 4 – 6 ]. Also, the occlusion of a face by other objects or varying levels of emotions may impede the performance of face recognition systems [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

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XAI-FR: Explainable AI-Based Face Recognition Using Deep Neural Networks

Rajpal

Sehra

Bagri

et al. 2022

Wireless Pers Commun

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Face Recognition aims at identifying or confirming an individual’s identity in a still image or video. Towards this end, machine learning and deep learning techniques have been successfully employed for face recognition. However, the response of the face recognition system often remains mysterious to the end-user. This paper aims to fill this gap by letting an end user know which features of the face has the model relied upon in recognizing a subject’s face. In this context, we evaluate the interpretability of several face recognizers employing deep neural networks namely, LeNet-5, AlexNet, Inception-V3, and VGG16. For this purpose, a recently proposed explainable AI tool–Local Interpretable Model-Agnostic Explanations (LIME) is used. Benchmark datasets such as Yale, AT &T dataset, and Labeled Faces in the Wild (LFW) are utilized for this purpose. We are able to demonstrate that LIME indeed marks the features that are visually significant features for face recognition.

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Section: Introductionmentioning

confidence: 66%

Section: Introductionmentioning

confidence: 99%

XAI-FR: Explainable AI-Based Face Recognition Using Deep Neural Networks

Rajpal

Sehra

Bagri

et al. 2022

Wireless Pers Commun

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“…In the paper [23] the author proposed an approach for 2D pose-variant face recognition using frontal view, they calculate a pose view angle and match the test images with rotated canonical face images, the test face is then warped to create a frontal view using landmark features, the distortion is also addressed by applying prior facial symmetry assumptions before matching with the frontal face. In the paper [24] the author proposed an integrated approach by combining feature pyramid and triplet loss technique, their approach reduce the computations by sharing the same backbone network.…”

Section: Related Workmentioning

confidence: 99%

Synthetic Image Generation using Conditional GAN Provided Single Sample Face Image

ALI,

Jadoon,

Kim

2024

Preprint

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Facial recognition systems are widely used systems around the globe. Their applications in real world scenarios like law enforcement, surveillance, ID card identification, information security, access control and many more has made them very important in today’s world. These systems have shown promising results and excellent performance; however, these systems need tremendous number of facial images for training before they are implemented in the real-world scenarios. The problem arises when we face lack of training images due to which the performance of such systems decreases exponentially. The problem becomes even worse when there is only one image per subject for training our facial recognition system, the probe image might contain variation like illumination, expression and disguise which are hard to be accurately recognized. Generative models have achieved great success for handling the lack of training data by generating synthetic data that resembles the original data. We have created a new method by fine tuning the basic Conditional Generative Adversarial Network (CGAN) to generate synthetic facial images provided only single image per subject. These synthetic facial images contain six variations that are smile, anger, stare, illumination and disguise. We increased the size of the dataset and then applied a convolutional neural network for facial recognition, which improved the accuracy of our system from 0.76 to 0.99.

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“…Another work using a holistic approach, in conjunction with face frontalization, was conducted in [2]. Petpairote et al [2] stated that a strict condition for a PIFR algorithm to be used in real-world scenarios is to require only one frontal image per subject within the face gallery. To achieve this, a face frontalization technique is proposed as a preprocessing step.…”

Section: B Work On Pifr From a Holistic Approachmentioning

confidence: 99%

“…T HE role of face recognition (FR) in real-world applications, including biometric authentication, surveillance, entertainment, and human-computer interaction, has expanded significantly in recent years [1], [2]. Significant progress has been made in automatic face recognition, with promising results achieved in both controlled and uncontrolled environments.…”

Section: Introductionmentioning

confidence: 99%

LS-SIFT: Enhancing the Robustness of SIFT During Pose-Invariant Face Recognition by Learning Facial Landmark Specific Mappings

Lin,

Linares Otoya

2024

IEEE Access

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The proper functioning of many real-world applications in biometrics and surveillance depends on the robustness of face recognition systems against pose, and illumination variations. In this work, we employ ensemble systems in conjunction with local descriptors to address pose-invariant face recognition (PIFR). Facial landmarks are detected during the first step with a two fold usage. The landmark locations are employed to perform head pose classification (HPC). HPC allows to select only the visible landmarks for further processing. Then, local descriptors are extracted from the selected landmarks within a face image. We are proposing a novel learned descriptor (LS-SIFT) to overcome the robustness limitations of SIFT against large viewpoint variability during face recognition. Second, the extracted descriptors are used to train the base learners comprising an ensemble system for each subject in a face database (one ensemble per subject, one base learner per landmark). A novel GMM-based base learner model, named Mahalanobis Similarity (MS), is introduced in this work. Finally, face recognition is performed based on the ensemble systems' outputs from all the subjects in a face database. During the experimental trials, SIFT and LS-SIFT are employed individually for local feature extraction, whereas GMM and MS are used to build the ensemble systems, in an independent manner, for further comparison. The whole PIFR system is tested on CMU-PIE, Multi-PIE, and FERET databases, outperforming most of the state-of-the-art works regarding images with pose angles in the range of ±90 o .

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2D Pose-Invariant Face Recognition Using Single Frontal-View Face Database

Cited by 11 publications

References 32 publications

XAI-FR: Explainable AI-Based Face Recognition Using Deep Neural Networks

XAI-FR: Explainable AI-Based Face Recognition Using Deep Neural Networks

Synthetic Image Generation using Conditional GAN Provided Single Sample Face Image

LS-SIFT: Enhancing the Robustness of SIFT During Pose-Invariant Face Recognition by Learning Facial Landmark Specific Mappings

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