Facial Expression Recognition: A Literature Survey

Nayak, Jyothi S.; Preeti, G; Vatsa, Manisha; Kadiri, Manisha Reddy; Samiksha, S

doi:10.14445/22312803/ijctt-v48p101

Cited by 7 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The object detection task is mainly composed of two problems. The first is to estimate where the object is located on the image with a bounding box, and the second is to recognise this object as belonging to a specific class [8], as illustrated in FIGURE 1. The major challenge is that it not known a priori how many objects there will be on the image to be detected.…”

Section: Real-time Object Detection With Deep Learningmentioning

confidence: 99%

Improving the Pedestrian Detection Performance in the Absence of Rich Training Datasets: A UK Case Study

Araujo¹,

Palade²,

Sedighi³

et al. 2022

AAIML

View full text Add to dashboard Cite

The World Health Organization estimates that well in excess of one million of lives are lost each year due to road traffic accidents. Since the human factor is the preeminent cause behind the traffic accidents, the development of reliable Advanced Driver Assistance Systems (ADASs) and Autonomous Vehicles (AVs) is seen by many as a possible solution to improve road safety. ADASs rely on the car perception system input that consists of camera(s), LIDAR and/or radar to detect pedestrians and other objects on the road. Hardware improvements as well as advances done in employing Deep Learning techniques for object detection popularized the Convolutional Neural Networks in the area of autonomous driving research and applications. However, the availability of quality and large datasets continues to be a most important contributor to the Deep Learning based model’s performance. With this in mind, this work analyses how a YOLO-based object detection architecture responded to limited data available for training and containing low-quality images. The work focused on pedestrian detection, since vulnerable road user’s safety is a major concern within AV and ADAS research communities. The proposed model was trained and tested on data gathered from Coventry, United Kingdom, city streets. The results show that the original YOLOv3 implementation reaches a 42.18% average precision (AP) and the main challenge was in detecting small objects. Network modifications were made and our final model, based on the original YOLOv3 implementation, achieved 51.6% AP. It is also demonstrated that the employed data augmentation approach is responsible for doubling the average precision of the final model.

show abstract

Section: Real-time Object Detection With Deep Learningmentioning

confidence: 99%

Improving the Pedestrian Detection Performance in the Absence of Rich Training Datasets: A UK Case Study

Araujo¹,

Palade²,

Sedighi³

et al. 2022

AAIML

View full text Add to dashboard Cite

show abstract

“…Three main challenges related to the FER system are needed to be considered. The first foremost challenge is to select features which, according to this study, should be picked carefully for better outcomes (Subramanyam & Fegade, 2013). The next challenge is about classification: It all depends upon the choice of the dataset which will improve the accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Texture Features-Based Robust Facial Expression Recognition

Goswami

Jalal

Negi

et al. 2021

International Journal of Computer Vision and Image Processing

View full text Add to dashboard Cite

Facial expression plays an important role in communicating emotions. In this paper, a robust method for recognizing facial expressions is proposed using the combination of appearance features. Traditionally, appearance features mainly divide any face image into regular matrices for the computation of facial expression recognition. However, in this paper, we have computed appearance features in specific regions by extracting facial components such as eyes, nose, mouth, and forehead, etc. The proposed approach mainly has five stages to detect facial expression viz. face detection and regions of interest extraction, feature extraction, pattern analysis using a local descriptor, the fusion of appearance features and finally classification using a Multiclass Support Vector Machine (MSVM). Results of the proposed method are compared with the earlier holistic representations for recognizing facial expressions, and it is found that the proposed method outperforms state-of-the-art methods.

show abstract

“…Machine learning and deep learning techniques are now widely used, the possibility of developing intelligent systems capable of properly recognizing emotions became a reality. Facial expression recognition (FER) technology is important in artificial intelligence (AI) since it assists in understanding the internal states of people [1]- [6]. With the worldwide spread of coronavirus disease 2019 (COVID- 19), wearing face masks while interaction in public is becoming a common behavior to protect against infection.…”

Section: Introductionmentioning

confidence: 99%

Facial expression recognition of masked faces using deep learning

Hdioud

Tirari

2023

IJ-AI

View full text Add to dashboard Cite

Facial expression recognition (FER) represents one of the most prevalent forms of interpersonal communication, which contains rich emotional information. But it became even more challenging during the times of COVID, where face masks became a mandatory protection measure, leading to the challenge of occluded lower-face during facial expression recognition. In this study, deep convolutional neural network (DCNN) represents the core of both our full-face FER system and our masked face FER model. The focus was on incorporating knowledge distillation in transfer learning between a teacher model, which is the full-face FER DCNN, and the student model, which is the masked face FER DCNN via the combination of both the loss from the teacher soft-labels vs the student soft labels and the loss from the dataset hard-labels vs the student hard-labels. The teacher-student architecture used FER2013 and a masked customized version of FER2013 as datasets to generate an accuracy of 69% and 61% respectively. Therefore, the study proves that the process of knowledge distillation may be used as a way for transfer learning and enhancing accuracy as a regular DCNN model (student only) would result in 46% accuracy compared to our approach (61% accuracy).

show abstract

Facial Expression Recognition: A Literature Survey

Cited by 7 publications

References 8 publications

Improving the Pedestrian Detection Performance in the Absence of Rich Training Datasets: A UK Case Study

Improving the Pedestrian Detection Performance in the Absence of Rich Training Datasets: A UK Case Study

A Texture Features-Based Robust Facial Expression Recognition

Facial expression recognition of masked faces using deep learning

Contact Info

Product

Resources

About