A Comparative Study on Unconstrained Hand Biometrics

Sierra, Alberto de Santos; Casanova, Javier Guerra; Ávila, Carmen Sánchez; Pozo, Gonzalo Bailador del

doi:10.1109/ichb.2011.6094312

Cited by 4 publications

(4 citation statements)

References 36 publications

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“…Increased electrodermal activity (EDA) has also been observed during TSST (Jezova et al, 2004). Finally, in other studies a relationship has been established between stress and blood pressure (BP), the electrical characteristics of the skin, and skin temperature (T) (De Santos-Sierra et al, 2011;Vinkers et al, 2013;Allen et al, 2014;Haufe et al, 2014).…”

Section: Introductionmentioning

confidence: 94%

Psychosomatic response to acute emotional stress in healthy students

et al. 2023

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The multidimensionality of the stress response has shown the complexity of this phenomenon and therefore the impossibility of finding a unique biomarker among the physiological variables related to stress. An experimental study was designed and performed to guarantee the correct synchronous and concurrent measure of psychometric tests, biochemical variables and physiological features related to acute emotional stress. The population studied corresponds to a group of 120 university students between 20 and 30 years of age, with healthy habits and without a diagnosis of chronic or psychiatric illnesses. Following the protocol of the experimental pilot, each participant reached a relaxing state and a stress state in two sessions of measurement for equivalent periods. Both states are correctly achieved evidenced by the psychometric test results and the biochemical variables. A Stress Reference Scale is proposed based on these two sets of variables. Then, aiming for a non-invasive and continuous approach, the Acute Stress Model correlated to the previous scale is also proposed, supported only by physiological signals. Preliminary results support the feasibility of measuring/quantifying the stress level. Although the results are limited to the population and stimulus type, the procedure and methodological analysis used for the assessment of acute stress in young people can be extrapolated to other populations and types of stress.

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Section: Introductionmentioning

confidence: 94%

Psychosomatic response to acute emotional stress in healthy students

et al. 2023

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“…ECG is the physiological signal that can record cardiac activity. As regulated by the ANS, during stress, the heart rate increases, and the heartbeat's standard deviation becomes larger (De Rosa, 2004;de Santos Sierra et al, 2011). These changes can be presented by ECG.…”

Section: Introductionmentioning

confidence: 99%

Real-time mental stress detection using multimodality expressions with a deep learning framework

et al. 2022

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Mental stress is becoming increasingly widespread and gradually severe in modern society, threatening people’s physical and mental health. To avoid the adverse effects of stress on people, it is imperative to detect stress in time. Many studies have demonstrated the effectiveness of using objective indicators to detect stress. Over the past few years, a growing number of researchers have been trying to use deep learning technology to detect stress. However, these works usually use single-modality for stress detection and rarely combine stress-related information from multimodality. In this paper, a real-time deep learning framework is proposed to fuse ECG, voice, and facial expressions for acute stress detection. The framework extracts the stress-related information of the corresponding input through ResNet50 and I3D with the temporal attention module (TAM), where TAM can highlight the distinguishing temporal representation for facial expressions about stress. The matrix eigenvector-based approach is then used to fuse the multimodality information about stress. To validate the effectiveness of the framework, a well-established psychological experiment, the Montreal imaging stress task (MIST), was applied in this work. We collected multimodality data from 20 participants during MIST. The results demonstrate that the framework can combine stress-related information from multimodality to achieve 85.1% accuracy in distinguishing acute stress. It can serve as a tool for computer-aided stress detection.

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“…The exploitation of the complementarity between biometric modalities increases the overall accuracy, improves the performance, reduces the vulnerability and therefore, results in more robust biometric systems which offer enhanced security against forgeries [52]. Due to different biometric traits can be extracted from hand images such as hand geometry [47,35], palmprint [97] or hand veins [208,191], hand can be considered multimodal itself and, thus, it is possible to improve the system performance by exploiting the information provided by its different traits. In this manner, some hand multimodal systems have been developed during the last years combining hand 202,98,95,43,22,174,96,190,101,81,68,121,62,149,69,209,206,193,108,85,148,152,139,66 [189,65,51,72,11,195,194,5,200,58,55,54,128,3,…”

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confidence: 99%

“…Later methods extract geometrical information about the hand included in the palm and fingers and are more widespread mainly due to its simplicity. Most simple feature vectors are composed by grayscale profiles [160,80] or finger widths [40,35,58,35], finger widths and lengths [124,123,55,54] or finger widths and lengths together with fingertips information [41,189]. Other works also add hand and palm information about size or angles together with finger meassurements [164,72,183,188,82,192,48].…”

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confidence: 99%

Analysis of image processing, machine learning and information fusion techniques for contact-less hand biometrics

Sánchez¹

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In this thesis different techniques of image processing, machine learning and information fusion have been analysed in relation to their applicability to contact-less hand biometrics. To this end, a modular and configurable system that explodes the multimodal nature of the hand to increase its robustness and accuracy have been designed, implemented and evaluated. Given the fact that different applications have different accuracy and time performance needs, the evaluation is aimed to provide a fair comparative of methods under different environmental conditions that helps to adapt the system to the specific requirements of a concrete final application.A correct hand segmentation is necessary to extract reliable and invariant biometric features. For this reason, a comparative of different segmentation methods that include well-known methods such as global thresholding and graph cuts as well as a novelty flooding-based method which combines different image-based segmentation approaches. These methods have been compared using diverse datasets of images which cover a wide spectrum of capturing conditions.On the other hand, a comprehensive evaluation of different palmprint feature extraction methods comprising Gabor and Sobel filters, Local Binary Patterns, Local Derivative Patterns and Curvelets has been carried out. Different parameter configurations have also been tested with the aim of finding out which arrangement provides better results for each method. In addition to palmprint, also hand geometry features have been extracted. This evaluation includes also two different feature matching approaches: distance-based and Support Vector Machines.In addition, it has also been evaluated the feasibility of combining different feature extraction methods to yield into a more precise and robust multimodal solution. Two different levels for fusing the biometric information have been compared: score-level and feature-level.Finally, an evaluation methodology that allows for a fair comparison between different methods has been proposed. In particular, an evaluation protocol is offered with the aim of not only obtaining an extensive evaluation of the complete system under different environmental conditions, and testing multiple combinations of methods for each module, but also providing a basis against which to compare future research.Keywords: Accuracy, Biometrics, Computation Requirements, Configurable, Curvelets, Distance, Evaluation, Feature-level fusion, Flooding, Fusion, Gabor, Graph-Cuts, Hand Geometry, Local Binary Patterns, Local Derivative Patterns, Modular, Multimodal, Palmprint, Score-level fusion, Segmentation, Sobel, Support Vector Machines, Thresholding, Varied Environmental Conditions. R E S U M E NEn esta tesis se han analizado diferentes técnicas de procesado de imagen, aprendizaje automático y fusión de la información en relación a su aplicabilidad a la biometría de mano sin contacto. Para ello se ha diseñado, implementado y evaluado un sistema modular y configurable que explota la naturaleza multimodal de la...

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A Comparative Study on Unconstrained Hand Biometrics

Cited by 4 publications

References 36 publications

Psychosomatic response to acute emotional stress in healthy students

Psychosomatic response to acute emotional stress in healthy students

Real-time mental stress detection using multimodality expressions with a deep learning framework

Analysis of image processing, machine learning and information fusion techniques for contact-less hand biometrics

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