| Title: |
Hybrid Siamese Network for Unconstrained Face Verification and Clustering under Limited Resources |
| Authors: |
Nehal K. Ahmed; Elsayed E. Hemayed; Magda B. Fayek |
| Source: |
Big Data and Cognitive Computing, Vol 4, Iss 19, p 19 (2020) |
| Publisher Information: |
MDPI AG |
| Publication Year: |
2020 |
| Collection: |
Directory of Open Access Journals: DOAJ Articles |
| Subject Terms: |
deep learning; face verification; face clustering; surveillance systems; Technology |
| Description: |
In this paper, we propose an unconstrained face verification approach that is dependent on Hybrid Siamese architecture under limited resources. The general face verification trend suggests that larger training datasets and/or complex architectures lead to higher accuracy. The proposed approach tends to achieve high accuracy while using a small dataset and a simple architecture by directly learn face’s similarity/dissimilarity from raw face pixels, which is critical for various applications. The proposed architecture has two branches; the first part of these branches is trained independently, while the other parts shared their parameters. A multi-batch algorithm is utilized for training. The training process takes a few hours on a single GPU. The proposed approach achieves near-human accuracy (98.9%) on the Labeled Faces in the Wild (LFW) benchmark, which is competitive with other techniques that are presented in the literature. It reaches 99.1% on the Arabian faces dataset. Moreover, features learned by the proposed architecture are used in building a face clustering system that is based on an updated version of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). To handle the cluster quality challenge, a novel post-clustering optimization procedure is proposed. It outperforms popular clustering approaches, like K-Means and spectral by 0.098 and up to 0.344 according to F1-measure. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
https://www.mdpi.com/2504-2289/4/3/19; https://doaj.org/toc/2504-2289; https://doaj.org/article/9adc89d7239f4a0fa5d85b0f63d84052 |
| DOI: |
10.3390/bdcc4030019 |
| Availability: |
https://doi.org/10.3390/bdcc4030019; https://doaj.org/article/9adc89d7239f4a0fa5d85b0f63d84052 |
| Accession Number: |
edsbas.9A7F624B |
| Database: |
BASE |