The design of isotherm face recognition technique based on nostril localization

Tzeng, Huan-Wen; Lee, He-Chin; Chen, Mei-Yung

doi:10.1109/icsse.2011.5961878

Cited by 7 publications

(7 citation statements)

References 11 publications

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“…Unlike in the case of images acquired in the visible spectrum, in which several salient facial features (such as the eyes and the mouth) can usually be reliably detected [30], [31], [32], most of the work to date supports the conclusion that salient facial feature localization in thermal images is significantly more challenging. Different approaches, which mainly focus on the eyes, were described by Tzeng et al [33], Arandjelović et al [25], Jin et al [34], Bourlai et al [35] and Martinez et al [36]. What is more, the effect of feature localization errors and thus registration errors seems to be greater for thermal than visible spectrum images.…”

Section: A Appearance-based Methodsmentioning

confidence: 99%

“…Owing to the increasing popularity of research into IR based recognition there are a number of approaches in the literature which we did not discuss explicitly. These include the geometric invariant moment based approaches of Abas and Ono [99], elastic graph matching based method of Hizem textitet al [100], isotherm based method of Tzeng et al [33], faceprints of Akhloufi and Bendada [101], fusion work of Toh et al [102], and others [103], [104], [105]. Specifically, we did not describe (i) minor extensions of the original approaches already surveyed and (ii) those methods which lack the weight of sufficient empirical evidence to support their competitiveness with the state-of-the-art at the time when they were first proposed.…”

Section: E Other Approachesmentioning

confidence: 99%

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Infrared face recognition: A literature review

Ghiass

Arandjelović

Bendada

et al. 2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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Abstract-Automatic face recognition (AFR) is an area with immense practical potential which includes a wide range of commercial and law enforcement applications, and it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in AFR continues to improve, benefiting from advances in a range of different fields including image processing, pattern recognition, computer graphics and physiology. However, systems based on visible spectrum images continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease their accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject.

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Section: A Appearance-based Methodsmentioning

confidence: 99%

Section: E Other Approachesmentioning

confidence: 99%

Infrared face recognition: A literature review

Ghiass

Arandjelović

Bendada

et al. 2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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“…Hussien et al [20] found Histogram of Gradients (HoG) to outperform Local Binary Patterns and Haar features when training a cascade classifier to detect eye regions. Tzeng et al [21] localized nose regions in video frames based on the temperature fluctuations caused by breathing.…”

Section: Related Work a Thermal Landmark Detectionmentioning

confidence: 99%

Visible-to-Thermal Transfer Learning for Facial Landmark Detection

Poster

Short

et al. 2021

IEEE Access

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There has been increasing interest in face recognition in the thermal infrared spectrum. A critical step in this process is face landmark detection. However, landmark detection in the thermal spectrum presents a unique set of challenges compared to in the visible spectrum: inherently lower spatial resolution due to longer wavelength, differences in phenomenology, and limited availability of labeled thermal face imagery for algorithm development and training. Thermal infrared imaging does have the advantage of being able to passively acquire facial heat signatures without the need for active or ambient illumination in low light and nighttime environments. In such scenarios, thermal imaging must operate by itself without corresponding/paired visible imagery. Mindful of this constraint, we propose visible-to-thermal parameter transfer learning using a coupled convolutional network architecture as a means to leverage visible face data when training a model for thermal-only face landmark detection. This differentiates our approach from models trained either solely on thermal images or models which require a fusion of visible and thermal images at test time. In this work, we implement and analyze four types of parameter transfer learning methods in the context of thermal face landmark detection: Siamese (shared) layers, Linear Layer Regularization (LLR), Linear Kernel Regularization (LKR), and Residual Parameter Transformations (RPT). These transfer learning approaches are compared against a baseline version of the network and an Active Appearance Model (AAM), both of which are trained only on thermal data. We achieve a 6.5% -9.5% improvement on the DEVCOM ARL Multi-modal Thermal Face Dataset and a 4% improvement on the RWTH Aachen University Thermal Face Dataset over the baseline model. We show that LLR, LKR, and RPT all result in improved thermal face landmark detection performance compared to the baseline and AAM, demonstrating that transfer learning leveraging visible spectrum data improves thermal face landmarking.

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“…A required step for automated ROI definition and tracking is the automated localization of salient facial regions that may serve as reference positions for ROI localization. In thermal images, many authors localize thermally well detectable regions such as eyes [13] or nostrils [12,14], however only very limited work has been published on the localization of multiple anatomic facial landmarks in thermal images. However, only a detection of a large number of landmarks yields sufficient localization information to allow ROI tracking across a large number of head poses.…”

Section: State Of the Artmentioning

confidence: 99%

A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings

Kopaczka

Breuer

Schock

et al. 2019

Sensors

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We present a system that utilizes a range of image processing algorithms to allow fully automated thermal face analysis under both laboratory and real-world conditions. We implement methods for face detection, facial landmark detection, face frontalization and analysis, combining all of these into a fully automated workflow. The system is fully modular and allows implementing own additional algorithms for improved performance or specialized tasks. Our suggested pipeline contains a histogtam of oriented gradients support vector machine (HOG-SVM) based face detector and different landmark detecion methods implemented using feature-based active appearance models, deep alignment networks and a deep shape regression network. Face frontalization is achieved by utilizing piecewise affine transformations. For the final analysis, we present an emotion recognition system that utilizes HOG features and a random forest classifier and a respiratory rate analysis module that computes average temperatures from an automatically detected region of interest. Results show that our combined system achieves a performance which is comparable to current stand-alone state-of-the-art methods for thermal face and landmark datection and a classification accuracy of 65.75% for four basic emotions.

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The design of isotherm face recognition technique based on nostril localization

Cited by 7 publications

References 11 publications

Infrared face recognition: A literature review

Infrared face recognition: A literature review

Visible-to-Thermal Transfer Learning for Facial Landmark Detection

A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings

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