Modified Convolutional Neural Network Architecture Analysis for Facial Emotion Recognition

Verma, Abhishek; Singh, Piyush; Alex, John Sahaya Rani

doi:10.1109/iwssip.2019.8787215

Cited by 27 publications

(8 citation statements)

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“…Therefore, the detection and extraction of emotional traits are also based on active-looking models or coding of facial actions treated by Ekman. The final presentation of the results shows a recognition accuracy with performance in the direction of reasoning emotions using the methods proposed and expressed by the graphical illustration in Figure 13, based on the data obtained [88,89]. Facial grimaces and uncontrolled reactions can lead to confusing reactions and guarantee invalid or non-compliant identification.…”

Section: Description Of Emotions Drivers Setup and Practical Scenariosmentioning

confidence: 96%

Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies

Zadobrischi

Dimian

2021

Remote Sensing

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Vehicle density and technological development increase the need for road and pedestrian safety systems. Identifying problems and addressing them through the development of systems to reduce the number of accidents and loss of life is imperative. This paper proposes the analysis and management of dangerous situations, with the help of systems and modules designed in this direction. The approach and classification of situations that can cause accidents is another feature analyzed in this paper, including detecting elements of a psychosomatic nature: analysis and detection of the conditions a driver goes through, pedestrian analysis, and maintaining a preventive approach, all of which are embedded in a modular architecture. The versatility and usefulness of such a system come through its ability to adapt to context and the ability to communicate with traffic safety systems such as V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure), V2X (vehicle-to-everything), and VLC (visible light communication). All these elements are found in the operation of the system and its ability to become a portable device dedicated to road safety based on (radio frequency) RF-VLC (visible light communication).

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Section: Description Of Emotions Drivers Setup and Practical Scenariosmentioning

confidence: 96%

Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies

Zadobrischi

Dimian

2021

Remote Sensing

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“…As an alternative to SSC, CNN has been also shown to yield great performance in many applications in the visual information processing field, such as facial recognition [9], [10], human motion recognition [11], skin lesions classification [12], and image classification [13], [14]. In addition, significant progress has been also made by deep learning for HSIC in recent years [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35].…”

Section: Iterative Random Training Sampling Convolutionalmentioning

confidence: 99%

Iterative Random Training Sampling Convolutional Neural Network for Hyperspectral Image Classification

Chang

Liang

2023

IEEE Trans. Geosci. Remote Sensing

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“…Verma et al proposed a convolution neural architecture called the Venturi architecture [27] to categorize the six basic plus neutral expressions from images of the KDEF database. The performance of the model is compared with rectangular architecture and the modified triangular architecture [28].…”

Section: A Deep Learning-based Approach To Emotion Categorizationmentioning

confidence: 99%

“…DL algorithms have been used [24], [25], [27] to categorize emotion from images. However, feeding DL algorithms with face images to perform end-to-end learning analyzes the color distribution of the pixels in an image, which may not generalize in a cross-database emotion categorization task as different databases might have varying color distribution of their pixels.…”

Section: A Deep Learning-based Approach To Emotion Categorizationmentioning

confidence: 99%

An Out-of-Distribution Attack Resistance Approach to Emotion Categorization

Shehu

Browne

Eisenbarth

2021

IEEE Trans. Artif. Intell.

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Deep neural networks are a powerful model for feature extraction. They produce features that enable state-of-theart performance on many tasks, including emotion categorization. However, their homogeneous representation of knowledge has made them prone to attacks, i.e. small modification in train or test data to mislead the models. Emotion categorization can usually be performed to be either in-distribution (train and test with the same dataset) or out-of-distribution (train on one or more dataset(s) and test on a different dataset). Our already developed landmark-based technique, which is robust for in-distribution improvement against attacks in emotion categorization, could translate to out-of-distribution classification problems. This is important as different databases might have different variations such as in color or level of expressiveness of emotion. We compared the landmark-based method with four state-of-theart deep models (EfficientNetB0, InceptionV3, ResNet50 and VGG19), as well as emotion categorization tools (i.e. Py-Feat and the Azure Face API) by performing a cross-database experiment across six commonly used databases, i.e. CK+, JAFFE, KDEF, NIMH-ChEF, RAF, and PICS databases. The landmark-based method has achieved a significantly higher accuracy, achieving an average of 47.44% compared with most of the deep networks (< 36%) and the emotion categorization tools (<37%) with considerably less execution time. This highlights that out-ofdistribution emotion categorization is a much harder task due to detecting underlying emotional cues than emotion categorization in-distribution where superficial patterns are detected to > 97% accuracy.Impact Statement-Recognising emotions from people's faces has real-world applications for computer-based perception as it is often vital for interpersonal communication. Emotion recognition tasks nowadays are addressed using deep learning models that model colour distribution so classify images rather than emotion. This homogeneous knowledge representation is in contrast to emotion categorization, which is hypothesised as more heterogeneous landmark-based. This is investigated through out-of-distribution emotion categorization problems, where the test samples are drawn from a different dataset to training images. Our landmark-based method achieves a significantly higher classification performance (on average) compared with four state-of-the-art deep networks (EfficientNetB0, InceptionV3, ResNet50 and VGG19), as well as other emotion categorization This paper is an expanded paper published in the proceedings of the IEEE RO-MAN Conference, which was held (in Naples, Italy) virtually as live interactive conference between 31 August -

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Modified Convolutional Neural Network Architecture Analysis for Facial Emotion Recognition

Cited by 27 publications

References 1 publication

Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies

Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies

Iterative Random Training Sampling Convolutional Neural Network for Hyperspectral Image Classification

An Out-of-Distribution Attack Resistance Approach to Emotion Categorization

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