Face Image Retrieval Using Discriminative Ternary Census Transform and Spatial Pyramid Matching

Hasnat, Abul; Halder, Sourav; Bhattacharjee, Debotosh; Nasipuri, Mita

doi:10.1007/978-981-13-8581-0_26

Cited by 1 publication

(1 citation statement)

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“…Deep convolutional network processes and the resulting features are then matched for retrieval. Hasnat et al ( 2019 ) proposed a new efficient method of face image retrieval system, i.e., discriminative ternary census transforms histogram (DTCTH) technique, which is specialized for capturing only the required information. Later, Shukla and Kanungo ( 2019 ) also introduced a new approach for face recognition and retrieval, in which a bag of features is used by extracting visual words with the help of the Gray Wolf Optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Automated facial characterization and image retrieval by convolutional neural networks

Shah,

Qureshi

et al. 2023

Front. Artif. Intell.

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IntroductionDeveloping efficient methods to infer relations among different faces consisting of numerous expressions or on the same face at different times (e.g., disease progression) is an open issue in imaging related research. In this study, we present a novel method for facial feature extraction, characterization, and identification based on classical computer vision coupled with deep learning and, more specifically, convolutional neural networks.MethodsWe describe the hybrid face characterization system named FRetrAIval (FRAI), which is a hybrid of the GoogleNet and the AlexNet Neural Network (NN) models. Images analyzed by the FRAI network are preprocessed by computer vision techniques such as the oriented gradient-based algorithm that can extract only the face region from any kind of picture. The Aligned Face dataset (AFD) was used to train and test the FRAI solution for extracting image features. The Labeled Faces in the Wild (LFW) holdout dataset has been used for external validation.Results and discussionOverall, in comparison to previous techniques, our methodology has shown much better results on k-Nearest Neighbors (KNN) by yielding the maximum precision, recall, F1, and F2 score values (92.00, 92.66, 92.33, and 92.52%, respectively) for AFD and (95.00% for each variable) for LFW dataset, which were used as training and testing datasets. The FRAI model may be potentially used in healthcare and criminology as well as many other applications where it is important to quickly identify face features such as fingerprint for a specific identification target.

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Section: Introductionmentioning

confidence: 99%