Automatic Gender Classification through Face Segmentation

Attique

et al. 2020

Sensors

Self Cite

Human face image analysis is an active research area within computer vision. In this paper we propose a framework for face image analysis, addressing three challenging problems of race, age, and gender recognition through face parsing. We manually labeled face images for training an end-to-end face parsing model through Deep Convolutional Neural Networks. The deep learning-based segmentation model parses a face image into seven dense classes. We use the probabilistic classification method and created probability maps for each face class. The probability maps are used as feature descriptors. We trained another Convolutional Neural Network model by extracting features from probability maps of the corresponding class for each demographic task (race, age, and gender). We perform extensive experiments on state-of-the-art datasets and obtained much better results as compared to previous results.

Section: Gender Classificationmentioning

confidence: 99%

A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks

Attique

et al. 2020

Sensors

Self Cite

“…These methods are dominant crowd modeling methods. Deep learning based methods (DLMs): As compared to TMLMs, recently introduced DLMs brought a large improvement in performance in various visual recognition tasks [ 89 , 90 , 91 , 92 , 93 ]. The TMLMs are based on handcrafted features, whereas, DLMs are more engineered.…”

Section: Approachesmentioning

confidence: 99%

Advances and Trends in Real Time Visual Crowd Analysis

Albattah

et al. 2020

Sensors

Self Cite

Real time crowd analysis represents an active area of research within the computer vision community in general and scene analysis in particular. Over the last 10 years, various methods for crowd management in real time scenario have received immense attention due to large scale applications in people counting, public events management, disaster management, safety monitoring an so on. Although many sophisticated algorithms have been developed to address the task; crowd management in real time conditions is still a challenging problem being completely solved, particularly in wild and unconstrained conditions. In the proposed paper, we present a detailed review of crowd analysis and management, focusing on state-of-the-art methods for both controlled and unconstrained conditions. The paper illustrates both the advantages and disadvantages of state-of-the-art methods. The methods presented comprise the seminal research works on crowd management, and monitoring and then culminating state-of-the-art methods of the newly introduced deep learning methods. Comparison of the previous methods is presented, with a detailed discussion of the direction for future research work. We believe this review article will contribute to various application domains and will also augment the knowledge of the crowd analysis within the research community.

“…The literature reported some excellent methods which segment a face image into various semantic parts such as nose, eyes, mouth, hair, skin, and back. These dense semantic class information are then used for modeling a framework for the above-mentioned face analysis tasks [2], [4], [25], [28].…”

Section: Face Segmentation Applicationsmentioning

confidence: 99%

“…The proposed model segmented an image into six semantic classes. The same work was extended by the authors to multi-tasks frameworks in some other papers [2]- [4], [24], [28]. The work proposed in [2], [3] was addressing three different tasks, including facial expression recognition, gender recognition, and head pose estimation.…”

Section: Hybrid Modelsmentioning

confidence: 99%

Face Segmentation: A Journey From Classical to Deep Learning Paradigm, Approaches, Trends, and Directions

Ahmad

et al. 2020

IEEE Access

Self Cite

Face segmentation represents an active area of research within the bio-metric community in particular and the computer vision community in general. Over the last two decades, methods for face segmentation have received increasing attention due to their diverse applications in several human-face image analysis tasks. Although many algorithms have been developed to address the problem, face segmentation is still a challenge not being completely solved, particularly for images taken in wild, unconstrained conditions. In this paper, we present a comprehensive review of face segmentation, focusing on methods for both the constrained and unconstrained environmental conditions. The article illustrates the advantages and disadvantages of previously proposed methods in state-of-the-art (SOA). The approaches presented comprise the seminal works on face segmentation and culminating in SOA approaches of the deep learning architecture. An extensive comparison of the previous approaches is intuitively presented, with a discussion of the potential directions for future research on the topic. We believe this comprehensive review and recap will contribute to a number of application domains, and will augment the knowledge of the research community.