Facial Recognition Datasets Research Articles

An Adaptive Fuzzy Ensemble Model for Facial Expression Recognition Using Poplar Optimization and CRNN

Facial expression recognition (FER) is one of the trending topics in the research field in recent years. Facial expressions are the direct way in which humans communicate their emotions and identifying these emotions requires complex cognitive skills. Because of continual advances in image processing and machine learning techniques, several novel strategies are presented each year. Their performance still needs to be enhanced, and it is a challenging task. So, we aim to propose a novel method called Ensemble Convolutional Recurrent Neural Network (CRNN), and this model is proposed to enhance the prediction accuracy of facial expressions. The methods such as GRU, LSTM, and CNN prediction outputs are incorporated. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is constructed as the ensemble model's topmost layer to adequately examine the few sub-models. The final output is predicted by the fuzzy inference system. The facial Recognition Dataset and EMOTIC dataset are utilized in the method for evaluating the results. The various performance metrics are utilized to test the efficiency of the proposed model such as precision, False Positive Rate, F1-score, True Positive Rate, and accuracy. Our proposed method achieves 99.52% for precision and 99.35% for F1-score. Compared with other existing such our proposed method achieves 99.75% and the value of the Area under the ROC curve (AUC) is 0.95.

Driver drowsiness detection in video sequences using hybrid selection of deep features

Monitoring driver’s drowsiness is a complex problem that involves many indicators whether behavioral or physiological. Drowsiness is a challenging problem that can lead to road disasters. Sleeping driver is more dangerous on the road than a speeding driver. Many statistics showed that one-fifth of road accidents in the world were due to driver fatigue, hence safety modules that can alert drowsy drivers in the hopes of reducing the risk of accidents are very important. This paper proposes a framework for driver drowsiness detection based on a computer vision solution. The proposed framework’s first task is to detect the driver’s face. A transfer learning is then performed for extracting the deep features from the driver’s face image using a pre-trained deep convolutional network model trained on a facial recognition dataset. The previous tasks are applied in a sliding temporal window (less than a second) in which the frames are sampled. In this work, 9 frames were the best choice. The extracted features of these frames represent the observation matrix. Then temporal feature aggregation is applied to construct the raw feature vector. To obtain the final feature vector, a proposed feature selection is applied to omit possible irrelevant features. The final feature vector is finally fed to a binary classifier to decide whether there is drowsiness or not. Extensive experiments are applied to NTHU Drowsy Driver Detection (NTHU-DDD) video dataset. The outcomes show the outperformance of the proposed approach compared with the state-of-the-art approaches.

Seeing infrastructure: race, facial recognition and the politics of data

ABSTRACT Facial recognition technology (FRT) has been widely studied and criticized for its racialising impacts and its role in the overpolicing of minoritised communities. However, a key aspect of facial recognition technologies is the dataset of faces used for training and testing. In this article, we situate FRT as an infrastructural assemblage and focus on the history of four facial recognition datasets: the original dataset created by W.W. Bledsoe and his team at the Panoramic Research Institute in 1963; the FERET dataset collected by the Army Research Laboratory in 1995; MEDS-I (2009) and MEDS-II (2011), the datasets containing dead arrestees, curated by the MITRE Corporation; and the Diversity in Faces dataset, created in 2019 by IBM. Through these four exemplary datasets, we suggest that the politics of race in facial recognition are about far more than simply representation, raising questions about the potential side-effects and limitations of efforts to simply ‘de-bias’ data.