Effective Descriptors based Face Recognition Technique for Robotic Surveillance Systems

Vinay, A.; Deshpande, Aditi R; Pranathi, B S; Jha, Harshita; Murthy, K. N. Balasubramanya; Natarajan, S.

doi:10.1016/j.procs.2018.07.080

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…In [31], the authors analyzed the performance of a PCA Eigenfaces-based face recognition system in a mobile robot to determine the optimal speed of the robot in a dynamic environment. Vinay et al [32] proposed a binary feature detector and descriptor named SPHORB (Spherical Oriented Fast and Rotated Brief) as an effective face recognition technique for robotic surveillance systems, but no robot was used during the experiments presented. The approach proposed in [33] looked for long-term face re-identification.…”

Section: Face Recognition In Social Service Robotsmentioning

confidence: 99%

Personal Guides: Heterogeneous Robots Sharing Personal Tours in Multi-Floor Environments

Rodriguez

Zabala

Marín-Reyes

et al. 2020

Sensors

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GidaBot is an application designed to setup and run a heterogeneous team of robots to act as tour guides in multi-floor buildings. Although the tours can go through several floors, the robots can only service a single floor, and thus, a guiding task may require collaboration among several robots. The designed system makes use of a robust inter-robot communication strategy to share goals and paths during the guiding tasks. Such tours work as personal services carried out by one or more robots. In this paper, a face re-identification/verification module based on state-of-the-art techniques is developed, evaluated offline, and integrated into GidaBot’s real daily activities, to avoid new visitors interfering with those attended. It is a complex problem because, as users are casual visitors, no long-term information is stored, and consequently, faces are unknown in the training step. Initially, re-identification and verification are evaluated offline considering different face detectors and computing distances in a face embedding representation. To fulfil the goal online, several face detectors are fused in parallel to avoid face alignment bias produced by face detectors under certain circumstances, and the decision is made based on a minimum distance criterion. This fused approach outperforms any individual method and highly improves the real system’s reliability, as the tests carried out using real robots at the Faculty of Informatics in San Sebastian show.

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Section: Face Recognition In Social Service Robotsmentioning

confidence: 99%

Personal Guides: Heterogeneous Robots Sharing Personal Tours in Multi-Floor Environments

Rodriguez

Zabala

Marín-Reyes

et al. 2020

Sensors

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“…Notably, SURF leverages box filters to swiftly compute operators, underscoring its efficiency in processing visual data. [24], [25], [26].…”

Section: Introductionmentioning

confidence: 99%

Facial-Based Autism Classification Using Support Vector Machine Method

Muhathir,

DR,

El Akmal

et al. 2024

IJCDS

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Autism Spectrum Disorder (ASD) is a complex neural developmental condition characterized by difficulties in communication, social interaction, and delayed brain development. Despite previous studies, there is a need to explore and enhance autism classification techniques using facial data. This research aims to classify individuals with autism based on facial images using the Support Vector Machine (SVM) method. It also evaluates the performance of SVM-based classification with HOG and SURF feature extraction, contributing to the identification of autism through facial features. A dataset of 200 facial images of students, including individuals with and without autism, was analyzed. The data was divided into 80:20 and 70:30 splits for training and testing purposes. SVM models with HOG and SURF feature extractions were evaluated using accuracy, precision, recall, and F1-Score metrics. The HOG-SVM and SURF-SVM models showed consistent performance in both data splitting scenarios. Accuracy values exceeded 0.88, and precision, recall, and F1-Score values were above 0.9. The 80:20 data split demonstrated improved performance, especially for the HOG-SVM model. Both HOG and SURF feature extraction methods showed good performance in classifying autism data. The SVM model with HOG achieved an accuracy of 0.95 in the 80:20 data split, while the SURF model achieved 0.9. Early autism detection based on facial data holds potential for use in student selection in elementary schools. However, the study has limitations due to limited data and the focus on accuracy alone. Future research can expand the data size, explore other feature extraction methods, and implement advanced deep learning techniques to improve classification performance and contribute further to autism detection based on facial data .

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Automatic classification of brain magnetic resonance images with hypercolumn deep features and machine learning

Akyol

2022

Phys Eng Sci Med

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Brain tumours are life-threatening and their early detection is very important in a patient's life. At the present time, magnetic resonance imaging is one of the methods used for detecting brain tumours. Expert decision support systems serve specialist physicians to make more accurate diagnoses by minimizing the errors arising from their subjective opinions in real clinical settings. The model proposed in this study detects important keypoints and then extracts hypercolumn deep features of these keypoints from some convolutional layers of VGG16. Finally, Random Forest and Logistic Regression classi ers are fed with a set of these features. Random Forest classi er offered the best performance with 94.84% accuracy, 95.48% sensitivity, 4.52% false negative rate, 94.19% speci city, and 94.35% precision in 5-fold crossvalidation in this study. Consequently, it is thought that the proposed model could contribute to eld experts by integrating it into computer-aided brain magnetic resonance imaging diagnosis systems.

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Effective Descriptors based Face Recognition Technique for Robotic Surveillance Systems

Cited by 4 publications

References 14 publications

Personal Guides: Heterogeneous Robots Sharing Personal Tours in Multi-Floor Environments

Personal Guides: Heterogeneous Robots Sharing Personal Tours in Multi-Floor Environments

Facial-Based Autism Classification Using Support Vector Machine Method

Automatic classification of brain magnetic resonance images with hypercolumn deep features and machine learning

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