A Review of Deep Convolutional Neural Networks in Mobile Face Recognition

With the emergence of deep learning, Convolutional Neural Network (CNN) models have been proposed to advance the progress of various applications, including face recognition, object detection, pattern recognition, and number plate recognition. The utilization of CNNs in these areas has considerably improved security and surveillance capabilities by providing automated recognition solutions, such as traffic surveillance, access control devices, biometric security systems, and attendance systems. However, there is still room for improvement in this field. This paper discusses several classic CNN models, such as LeNet-5, AlexNet, VGGNet, GoogLeNet, and ResNet, as well as lightweight models for mobile-based applications, such as MobileNet, ShuffleNet, and EfficientNet. Additionally, deep CNN-based face recognition models, such as DeepFace, DeepID, FaceNet, and SphereFace, are explored, along with their architectural characteristics, advantages, disadvantages, and recognition accuracy. The results indicate that many scholars are researching lightweight face recognition, but applying it to mobile devices is impractical due to high computational costs. Furthermore, noise label learning is not robust in actual scenarios, and unlabeled face learning is expensive in manual labeling. Finally, this paper concludes with a discussion of the current problems faced by face recognition technology and its potential future directions for development

Pedestrian Attribute Recognition: A Survey

Recognizing pedestrian attributes is an important task in computer vision community due to it plays an important role in video surveillance. Many algorithms has been proposed to handle this task. The goal of this paper is to review existing works using traditional methods or based on deep learning networks. Firstly, we introduce the background of pedestrian attributes recognition (PAR, for short), including the fundamental concepts of pedestrian attributes and corresponding challenges. Secondly, we introduce existing benchmarks, including popular datasets and evaluation criterion. Thirdly, we analyse the concept of multi-task learning and multi-label learning, and also explain the relations between these two learning algorithms and pedestrian attribute recognition. We also review some popular network architectures which have widely applied in the deep learning community. Fourthly, we analyse popular solutions for this task, such as attributes group, part-based, \emph{etc}. Fifthly, we shown some applications which takes pedestrian attributes into consideration and achieve better performance. Finally, we summarized this paper and give several possible research directions for pedestrian attributes recognition. The project page of this paper can be found from the following website: \url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey: https://sites.google.com/view/ahu-pedestrianattributes

Evaluation of deep learning transformers models for brain stroke lesions automatic segmentation

Brain stroke represents a leading cause in long-term disability worldwide, stroke rehabilitation research is focused on the understating of the relationship between brain, behavior, and recovery, using as a basis brain changes generated after a stroke, this allows for precise diagnostic and possible predictions in terms of functional outcomes. Neuroimaging represents the main resource for brain stroke research and therapies, it is of particular interest high-resolution T1- weighted (T1w) anatomical MRIs, which are used to evaluate/examine structural brain changes after stroke episodes. Several techniques have been developed in order to accurately calculate or approximate the percentage between lesions and critical brain structures, this step constitutes a paramount step for precise lesion annotation. Despite the technological progress or advance, to date, manual lesion tracing by a team of experts in neuroimaging remains as the gold standard to draw valid clinical inferences for lesion segmentation. The following work proposes a review of the machine and deep learning models that have been developed focusing in the transformers algorithm which is a state of the art method based on the self attention mechanism that has outperformed recurrent neural networks in terms of evaluation metrics such as the dice value, being able to capture long distant dependencies which is a fundamental step when processing 3D volumes, formed by a stacked 2D MRI images. The models were tested using the ATLAS dataset (Anatomical tracing of lesions after stroke) which is an open source data set of T1-weighted MRIs with manual segmented brain lesions.Brain stroke represents a leading cause in long-term disability worldwide, stroke rehabilitation research is focused on the understating of the relationship between brain, behavior, and recovery, using as a basis brain changes generated after a stroke, this allows for precise diagnostic and possible predictions in terms of functional outcomes. Neuroimaging represents the main resource for brain stroke research and therapies, it is of particular interest high-resolution T1- weighted (T1w) anatomical MRIs, which are used to evaluate/examine structural brain changes after stroke episodes. Several techniques have been developed in order to accurately calculate or approximate the percentage between lesions and critical brain structures, this step constitutes a paramount step for precise lesion annotation. Despite the technological progress or advance, to date, manual lesion tracing by a team of experts in neuroimaging remains as the gold standard to draw valid clinical inferences for lesion segmentation. The following work proposes a review of the machine and deep learning models that have been developed focusing in the transformers algorithm which is a state of the art method based on the self attention mechanism that has outperformed recurrent neural networks in terms of evaluation metrics such as the dice value, being able to capture long distant dependencies which is a fundamental step when processing 3D volumes, formed by a stacked 2D MRI images. The models were tested using the ATLAS dataset (Anatomical tracing of lesions after stroke) which is an open source data set of T1-weighted MRIs with manual segmented brain lesions