@ARTICLE{Balasubramanian_C_An_2024,
 author={Balasubramanian, C and Raja Sekar, J},
 volume={72},
 number={2},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e148942},
 howpublished={online},
 year={2024},
 abstract={With the advent of social media, the volume of photographs uploaded on the internet has increased exponentially. The task of efficiently recognizing and retrieving human facial images is inevitable and essential at this time. In this work, a feature selection approach for recognizing and retrieving human face images using hybrid cheetah optimization algorithm is proposed. The deep feature extraction from the images is done using deep convolutional neural networks. Hybrid cheetah optimization algorithm, an improvised version of cheetah optimization algorithm fused with genetic algorithm is used, to choose optimum features from the extracted deep features. The chosen features are used for finding the best-matching images from the image database. The image matching is performed by approximate nearest neighbor search for the query image over the image database and similar images are retrieved. By constructing a k-NN graph for the images, the efficiency of image retrieval is enhanced. The proposed system performance is evaluated against benchmark datasets such as LFW, MultiePie, ColorFERET, DigiFace-1M and CelebA. The evaluation results show that the proposed methodology is superior to various existing methodologies.},
 type={Article},
 title={An improved facial image retrieval using hybrid cheetah optimization algorithm},
 URL={http://journals.pan.pl/Content/130023/PDF-MASTER/BPASTS_2023_72_2_3649.pdf},
 doi={10.24425/bpasts.2024.148942},
 keywords={deep learning, feature selection, facial image retrieval, cheetah optimization, approximate nearest neighbor},
}