Performance evaluation of state-of-the-art 2D face recognition algorithms on real and synthetic masked face datasets

Face recognition systems based on Convolutional neural networks have recorded unprecedented performance for multiple benchmark face datasets. Due to the Covid-19 outbreak, people are now compelled to wear face masks to reduce the virus's transmissibility. Recent research shows that when given the masked face recognition scenario, which imposes up to 70% occlusion of the face area, the performance of the FR algorithms degrades by a significant margin. This paper presents an experimental evaluation of a subset of the MFD-Kaggle and Masked-LFW (MLFW) datasets to explore the effects of face mask occlusion against implementing seven state-of-the-art FR models. Experiments on MFD-Kaggle show that the accuracy of the best-performing model, VGGFace degraded by almost 40%, from 82.1% (unmasked) to 40.4% (masked). On a larger-scale dataset MLFW, the impact of mask-wearing on FR models was also up to 50%. We trained and evaluated a proposed Mask Face Recognition (MFR) model whose performance is much better than the SOTA algorithms. The SOTA algorithms studied are unusable in the presence of face masks, and MFR performance is slightly degraded without face masks. This show that more robust FR models are required for real masked face applications while having a large-scale masked face dataset

Efficient region of interest based metric learning for effective open world deep face

Face Recognition (FR) has recently gained traction as a widely used biometric for securitybased applications such as facial recognition payment. The widespread use is due to improvements in deep convolutional neural networks (CNN) and large datasets. However, FR is still an ill-posed problem, especially in an open world scenario. Existing FR methods require finetuning, classifier retraining, or global metric learning to improve the performance for effective domain adaptation. It incurs an undesirable downtime. Open world FR must identify the persons for whom the FR model is not trained. It also produces imbalanced pairs, giving a false sense of high performance. The popular fixed threshold strategies, such as σ values, also lead to sub-optimal performance. This paper proposes a fast and efficient threshold adapter algorithm using an effective Region of Interest (ROI) setting for metric learning. It uses five different ROI schemes to find an adaptive threshold in real-time. The algorithm also determines the FR model quality and usability after new enrolments. To establish the effectiveness, we investigated various threshold finding strategies for five state-of-the-art face recognition algorithms for open world adaptation on different datasets.We also proposed a novel performance evaluation metric for FR algorithms on imbalanced datasets. Experimental results demonstrated that the proposed metric learning is up to 12 times faster than the nearest competitor while reporting higher accuracy and fewer errors. The study suggests that the F1-score is vital as a performance indicator for imbalanced pair evaluation, and accuracy at the highest reported F1-score is the desired metric for benchmarking FR algorithms in open world

Power of alignment: exploring the effect of face alignment on ASD diagnosis using facial images

Autism Spectrum Disorder (ASD) is a developmental disorder that impacts social communication and conduct.ASD lacks standard treatment protocols or medication,thus early identification and proper intervention are the most effective procedures to treat this disorder. Artificial intelligence could be a very effective tool to be used in ASD diagnosis as this is free from human bias. This research examines the effect of face alignment for the early diagnosis of Autism Spectrum Disorder (ASD) using facial images with the possibility that face alignment can improve the prediction accuracy of deep learningalgorithms.This work uses the SOTA deep learning-based face alignment algorithm MTCNN to preprocess the raw data. In addition, the impactsof facial alignmenton ASD diagnosisusing facial imagesare investigated using state-of-the-art CNN backbones such as ResNet50, Xception, and MobileNet. ResNet50V2 achieves the maximum prediction accuracy of 93.97% and AUC of 96.33% with the alignment of training samples, which is a substantial improvement over previous research. This research paves the way for a data-centric approach that can be applied to medical datasets in order to improve the efficacy of deep neural network algorithms used to develop smart medical devices for the benefit of mankin

Effective pneumonia detection using ResNet based transfer learning

Pneumonia is a deadly lungs disease known as silent killer is due to bacterial, viral, or fungal infection and causes lung alveoli to fill with pus or fluids. The most common diagnostic tool for pneumonia is Chest X-rays. However, due to several other medical conditions in the lungs, such as volume loss, bleeding,lung cancer,fluid overload,post-radiation or surgery, the diagnosis of pneumonia using chest X-rays becomes very complicated. Therefore, there is a dire need for computer-aided diagnosis systems to assist clinicians in making better decisions. This work proposes an effective, deep convolutional neural network with ResNet-50 architecture for pneumonia detection ResNet has performed quite well on the image recognition task and was a winner of the ImageNet challenge.A pre-trained ResNet-50 model is re-trained with the use of Transfer Learning on two different datasets of chest x-ray images. ResNet-50 based diagnostics model is found useful for pneumonia diagnostics despite significant variations in two datasets. The trained model has achieved an accuracy of 96.76%, which is at par with state-of-the-art techniques available. RSNA dataset, with five times more images than the Chest X-ray Image dataset, took very little time for training. Also, because of the use of the Transfer Learning technique, both the models were able to learn the significant features of pneumonia with only 50% training dataset size.However, the model can be improvised by using more deeper networks. Work can be extended to detect and classify both lung cancer and pneumonia using X-ray images

Integration of Stereo Vision and MOOS-IvP for Enhanced Obstacle Detection and Navigation in Unmanned Surface Vehicles

This paper addresses the development of a stereo vision-based obstacle avoidance system using MOOS-IvP for small and medium-sized Unmanned Surface Vehicles (USVs). Existing methods predominantly rely on optical sensors such as LiDAR and cameras to discern maritime obstacles within the short- to mid-range distances. Nonetheless, conventional cameras encounter challenges in water conditions that curtail their effectiveness in localizing obstacles and planning paths. Furthermore, LiDAR has limitations regarding angular resolution and identifying objectness due to data sparsity. To overcome these limitations, our proposed system leverages a stereo camera equipped with enhanced angular resolution to augment situational awareness. The system employs recursive estimation techniques to ascertain the position and dimensions of proximate obstacles, transmitting this information to the onboard control unit, where MOOS-IvP behaviour-based software produces navigation decisions. Through the real-time fusion of data obtained from the stereo vision system and navigational data, the system is able to achieve Enhance Situational Awareness (ESA) and facilitate well-informed navigation decisions. Developing a state-of-the-art maritime object detection technique, the system adeptly identifies obstacles and swiftly responds via a vision integration protocol. During field tests, our system proves the efficacy of the proposed ESA approach. This paper also presents a comprehensive analysis and discussion of the results derived from deploying the proposed system on the Suraya Surveyor USV platform across numerous scenarios featuring diverse obstacles. The results from these various scenarios demonstrate the system’s accurate obstacle detection capabilities under challenging conditions and highlight its significant potential for safe USV operations

Empirical study of autism spectrum disorder diagnosis using facial images by improved transfer learning approach

Autism spectrum disorder (ASD) is a neurological illness characterized by deficits in cognition, physical activities, and social skills. There is no specific medication to treat this illness; only early intervention can improve brain functionality. Since there is no medical test to identify ASD, a diagnosis might be challenging. In order to determine a diagnosis, doctors consider the child’s behavior and developmental history. The human face can be used as a biomarker as it is one of the potential reflections of the brain and thus can be used as a simple and handy tool for early diagnosis. This study uses several deep convolutional neural network (CNN)-based transfer learning approaches to detect autistic children using the facial image. An empirical study is conducted to select the best optimizer and set of hyperparameters to achieve better prediction accuracy using the CNN model. After training and validating with the optimized setting, the modified Xception model demonstrates the best performance by achieving an accuracy of 95% on the test set, whereas the VGG19, ResNet50V2, MobileNetV2, and EfficientNetB0 achieved 86.5%, 94%, 92%, and 85.8%, accuracy, respectively. Our preliminary computational results demonstrate that our transfer learning approaches outperformed existing methods. Our modified model can be employed to assist doctors and practitioners in validating their initial screening to detect children with ASD disease

Efficient deep learning-based data-centric approach for autism spectrum disorder diagnosis from facial images using explainable AI

The research describes an effective deep learning-based, data-centric approach for diagnosing autism spectrum disorder from facial images. To classify ASD and non-ASD subjects, this method requires training a convolutional neural network using the facial image dataset. As a part of the data-centric approach, this research applies pre-processing and synthesizing of the training dataset. The trained model is subsequently evaluated on an independent test set in order to assess the performance matrices of various data-centric approaches. The results reveal that the proposed method that simultaneously applies the pre-processing and augmentation approach on the training dataset outperforms the recent works, achieving excellent 98.9% prediction accuracy, sensitivity, and specificity while having 99.9% AUC. This work enhances the clarity and comprehensibility of the algorithm by integrating explainable AI techniques, providing clinicians with valuable and interpretable insights into the decision-making process of the ASD diagnosis model