Face Recognition using Filtered Eoh-sift☆
A. Vinay,Ganesh Kathiresan,Durga Akhil Mundroy,H. Nihar Nandan,Chetna Sureka,K. N. Balasubramanya Murthy,Sriraam Natarajan +6 more
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TL;DR: This paper shows that, with two filters which when used in a specific order, significantly boost the potency of the EOH-SIFT approach to identify faces, this approach has given very promising results when tested on the ORL database.
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About: This article is published in Procedia Computer Science. The article was published on 01 Jan 2016. and is currently open access. The article focuses on the topics: Facial recognition system.
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Citations
Grey Wolf optimisation-based feature selection and classification for facial emotion recognition
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TL;DR: An effective performance analysis of the proposed as well as the conventional methods such as convolutional neural network, NN-Levenberg-Marquardt, N nN-Gradient Descent, N N-Evolutionary Algorithm, Nn-firefly, and N n-Particle Swarm Optimisation is provided by evaluating few performance measures and thereby, the effectiveness of the suggested strategy over the conventional method is validated.
161
Multi-Faces Recognition Process Using Haar Cascades and Eigenface Methods
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- 10 May 2018
TL;DR: The proposed face recognition process was done using a hybrid process of Haar Cascades and Eigenface methods, which can detect multiple faces in a single detection process and was able to recognize multiple faces with 91.67% accuracy level.
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Enhanced Facial Emotion Recognition by Optimal Descriptor Selection with Neural Network
TL;DR: Facial Emotion Recognition (FER) is the approach of detecting emotions of humans from facial expressions that is based on explicit and implicit recognition of human facial expressions.
37
Performance Analysis of Human Face Recognition Techniques
Sharmila,Raman Sharma,Dhanajay Kumar,Vaishali Puranik,Kritika Gautham +4 more
- 18 Apr 2019
TL;DR: A comprehensive overview of the state of the art research work on face detection and recognition is presented and the performance analysis of various face recognition approaches are discussed and concluded with future research direction.
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Surveillance video face recognition with single sample per person based on 3D modeling and blurring
TL;DR: Experimental results indicated that the proposed method for generating more train samples was effective and could be considered to be applied in intelligent video monitoring system.
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TL;DR: This paper examines (and improves upon) the local image descriptor used by SIFT, and demonstrates that the PCA-based local descriptors are more distinctive, more robust to image deformations, and more compact than the standard SIFT representation.
ASIFT: A New Framework for Fully Affine Invariant Image Comparison
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TL;DR: The proposed affine-SIFT (ASIFT), simulates all image views obtainable by varying the two camera axis orientation parameters, namely, the latitude and the longitude angles, left over by the SIFT method, and will be mathematically proved to be fully affine invariant.
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CSIFT: A SIFT Descriptor with Color Invariant Characteristics
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TL;DR: The built Colored SIFT (CSIFT) is more robust than the conventional SIFT with respect to color and photometrical variations and the evaluation results support the potential of the proposed approach.
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