5,477 research outputs found
Reconnaissance de l'émotion thermique
Pour améliorer les interactions homme-ordinateur dans les domaines de la santé, de l'e-learning et des jeux vidéos, de nombreux chercheurs ont étudié la reconnaissance des émotions à partir des signaux de texte, de parole, d'expression faciale, de détection d'émotion ou d'électroencéphalographie (EEG). Parmi eux, la reconnaissance d'émotion à l'aide d'EEG a permis une précision satisfaisante. Cependant, le fait d'utiliser des dispositifs d'électroencéphalographie limite la gamme des mouvements de l'utilisateur. Une méthode non envahissante est donc nécessaire pour faciliter la détection des émotions et ses applications. C'est pourquoi nous avons proposé d'utiliser une caméra thermique pour capturer les changements de température de la peau, puis appliquer des algorithmes d'apprentissage machine pour classer les changements d'émotion en conséquence. Cette thèse contient deux études sur la détection d'émotion thermique avec la comparaison de la détection d'émotion basée sur EEG. L'un était de découvrir les profils de détection émotionnelle thermique en comparaison avec la technologie de détection d'émotion basée sur EEG; L'autre était de construire une application avec des algorithmes d'apprentissage en machine profonds pour visualiser la précision et la performance de la détection d'émotion thermique et basée sur EEG. Dans la première recherche, nous avons appliqué HMM dans la reconnaissance de l'émotion thermique, et après avoir comparé à la détection de l'émotion basée sur EEG, nous avons identifié les caractéristiques liées à l'émotion de la température de la peau en termes d'intensité et de rapidité. Dans la deuxième recherche, nous avons mis en place une application de détection d'émotion qui supporte à la fois la détection d'émotion thermique et la détection d'émotion basée sur EEG en appliquant les méthodes d'apprentissage par machine profondes - Réseau Neuronal Convolutif (CNN) et Mémoire à long court-terme (LSTM). La précision de la détection d'émotion basée sur l'image thermique a atteint 52,59% et la précision de la détection basée sur l'EEG a atteint 67,05%. Dans une autre étude, nous allons faire plus de recherches sur l'ajustement des algorithmes d'apprentissage machine pour améliorer la précision de détection d'émotion thermique.To improve computer-human interactions in the areas of healthcare, e-learning and video
games, many researchers have studied on recognizing emotions from text, speech, facial
expressions, emotion detection, or electroencephalography (EEG) signals. Among them,
emotion recognition using EEG has achieved satisfying accuracy. However, wearing
electroencephalography devices limits the range of user movement, thus a noninvasive method
is required to facilitate the emotion detection and its applications. That’s why we proposed using
thermal camera to capture the skin temperature changes and then applying machine learning
algorithms to classify emotion changes accordingly. This thesis contains two studies on thermal
emotion detection with the comparison of EEG-base emotion detection. One was to find out the
thermal emotional detection profiles comparing with EEG-based emotion detection technology;
the other was to implement an application with deep machine learning algorithms to visually
display both thermal and EEG based emotion detection accuracy and performance. In the first
research, we applied HMM in thermal emotion recognition, and after comparing with EEG-base
emotion detection, we identified skin temperature emotion-related features in terms of intensity
and rapidity. In the second research, we implemented an emotion detection application
supporting both thermal emotion detection and EEG-based emotion detection with applying the
deep machine learning methods – Convolutional Neutral Network (CNN) and LSTM (Long-
Short Term Memory). The accuracy of thermal image based emotion detection achieved 52.59%
and the accuracy of EEG based detection achieved 67.05%. In further study, we will do more
research on adjusting machine learning algorithms to improve the thermal emotion detection
precision
Recent Advances in Deep Learning Techniques for Face Recognition
In recent years, researchers have proposed many deep learning (DL) methods
for various tasks, and particularly face recognition (FR) made an enormous leap
using these techniques. Deep FR systems benefit from the hierarchical
architecture of the DL methods to learn discriminative face representation.
Therefore, DL techniques significantly improve state-of-the-art performance on
FR systems and encourage diverse and efficient real-world applications. In this
paper, we present a comprehensive analysis of various FR systems that leverage
the different types of DL techniques, and for the study, we summarize 168
recent contributions from this area. We discuss the papers related to different
algorithms, architectures, loss functions, activation functions, datasets,
challenges, improvement ideas, current and future trends of DL-based FR
systems. We provide a detailed discussion of various DL methods to understand
the current state-of-the-art, and then we discuss various activation and loss
functions for the methods. Additionally, we summarize different datasets used
widely for FR tasks and discuss challenges related to illumination, expression,
pose variations, and occlusion. Finally, we discuss improvement ideas, current
and future trends of FR tasks.Comment: 32 pages and citation: M. T. H. Fuad et al., "Recent Advances in Deep
Learning Techniques for Face Recognition," in IEEE Access, vol. 9, pp.
99112-99142, 2021, doi: 10.1109/ACCESS.2021.309613
Ubiquitous Technologies for Emotion Recognition
Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions
Drowsiness Detection Based on Yawning Using Modified Pre-trained Model MobileNetV2 and ResNet50
Traffic accidents are fatal events that need special attention. According to research by the National Transportation Safety Committee, 80% of traffic accidents are caused by human error, one of which is tired and drowsy drivers. The brain can interpret the vital fatigue of a drowsy driver sign as yawning. Therefore, yawning detection for preventing drowsy drivers’ imprudent can be developed using computer vision. This method is easy to implement and does not affect the driver when handling a vehicle. The research aimed to detect drowsy drivers based on facial expression changes of yawning by combining the Haar Cascade classifier and a modified pre-trained model, MobileNetV2 and ResNet50. Both proposed models accurately detected real-time images using a camera. The analysis showed that the yawning detection model based on the ResNet50 algorithm is more reliable, with the model obtaining 99% of accuracy. Furthermore, ResNet50 demonstrated reproducible outcomes for yawning detection, considering having good training capabilities and overall evaluation results
Human Face Recognition Based on Local Ternary Pattern and Singular Value Decomposition
هناك العديد من وسائل التحقق الحيوية البشرية المستخدمة في الوقت الحاضر، واحد من أهم هذه الوسائل هو الوجه. هناك العديد من التقنيات المقترجة للتعرف على الوجوه، لكنها بشكل عام لا تزال تواجه مجموعة متنوعة من التحديات للتعرف على الوجوه في الصور الملتقطة في بيئة غير مسيطر عليها، وكذلك في تطبيقات العالم الحقيقي. بعض هذه التحديات هي اختلاف الوضع، اختفاء جزء من الوجه، تعبيرات الوجه، الإضاءة ، الإضاءة السيئة، جودة الصورة .. إلخ. تحدث هذه التقنيات بتقنيات جديدة باستمرار. في هذا البحث، تم استخدام تحليل القيمة المفردة لاستخراج مصفوفة الميزات للتعرف على الوجوه وتصنيفها. الصورة الملونة المدخلة يتم تحويلها إلى صورة ذات تدرج رمادي، ثم تتحول إلى نمط اخر باستخدام LTP قبل تقسيم الصورة إلى ستة عشر كتلة رئيسية، كل كتلة من هذه الكتل الستة عشر تقسم ايضا إلى ثلاثين كتلة فرعية. لكل كتلة فرعية، يتم تطبيق تحويل SVD، ويتم حساب القيمة الأكبر في المصفوفة القطرية التي تُستخدم لإنشاء مصفوفة ميزات بحجم 16 × 30. يتم تنفيذ التصنيف من خلال شبكة عصبية، حيث يتم اختيار متجه بعدد قيم يبلغ 16 قيمة كمدخل الى الشبكة العصبية. وصل كفاءة الخوارزمية المقترحة إلى 97٪ عند استخدام قاعدة بيانات FEI البرازيلية. علاوة على ذلك، يعد أداء هذه الخوارزمية واعدا عند مقارنتها بأحدث الأساليب الحديثة فضلا عن انها حلت بعض التحديات مثل الإضاءة وتعبيرات الوجه.There is various human biometrics used nowadays, one of the most important of these biometrics is the face. Many techniques have been suggested for face recognition, but they still face a variety of challenges for recognizing faces in images captured in the uncontrolled environment, and for real-life applications. Some of these challenges are pose variation, occlusion, facial expression, illumination, bad lighting, and image quality. New techniques are updating continuously. In this paper, the singular value decomposition is used to extract the features matrix for face recognition and classification. The input color image is converted into a grayscale image and then transformed into a local ternary pattern before splitting the image into the main sixteen blocks. Each block of these sixteen blocks is divided into more to thirty sub-blocks. For each sub-block, the SVD transformation is applied, and the norm of the diagonal matrix is calculated, which is used to create the 16x30 feature matrix. The sub-blocks of two images, (thirty elements in the main block) are compared with others using the Euclidean distance. The minimum value for each main block is selected to be one feature input to the neural network. Classification is implemented by a backpropagation neural network, where a 16-feature matrix is used as input to the neural network. The performance of the current proposal was up to 97% when using the FEI (Brazilian) database. Moreover, the performance of this study is promised when compared with recent state-of-the-art approaches and it solves some of the challenges such as illumination and facial expression
Micro-attention for micro-expression recognition
Micro-expression, for its high objectivity in emotion detection, has emerged to be a promising modality in affective computing. Recently, deep learning methods have been successfully introduced into the micro-expression recognition area. Whilst the higher recognition accuracy achieved, substantial challenges in micro-expression recognition remain. The existence of micro expression in small-local areas on face and limited size of available databases still constrain the recognition accuracy on such emotional facial behavior. In this work, to tackle such challenges, we propose a novel attention mechanism called micro-attention cooperating with residual network. Micro-attention enables the network to learn to focus on facial areas of interest covering different action units. Moreover, coping with small datasets, the micro-attention is designed without adding noticeable parameters while a simple yet efficient transfer learning approach is together utilized to alleviate the overfitting risk. With extensive experimental evaluations on three benchmarks (CASMEII, SAMM and SMIC) and post-hoc feature visualizations, we demonstrate the effectiveness of the proposed micro-attention and push the boundary of automatic recognition of micro-expression
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