Locality-Sensitive Hashing of Soft Biometrics for Efficient Face Image Database Search and Retrieval

Abstract

As multimedia technology has advanced in recent years, the use of enormous image libraries has dramatically expanded. In applications for image processing, image retrieval has emerged as a crucial technique. Content-based face image retrieval is a well-established technology in many real-world applications, such as social media, where dependable retrieval capabilities are required to enable quick search among large numbers of images. Humans frequently use faces to recognize and identify individuals. Face recognition from official or personal photos is becoming increasingly popular as it can aid crime detectives in identifying victims and criminals. Furthermore, a large number of images requires a large amount of storage, and the process of image comparison and matching, consequently, takes longer. Hence, the query speed and low storage consumption of hash-based image retrieval techniques have garnered a considerable amount of interest. The main contribution of this work is to try to overcome the challenge of performance improvement in image retrieval by using locality-sensitive hashing (LSH) for retrieving top-matched face images from large-scale databases. We use face soft biometrics as a search input and propose an effective LSH-based method to replace standard face soft biometrics with their corresponding hash codes for searching a large-scale face database and retrieving the top-k of the matching face images with higher accuracy in less time. The experimental results, using the Labeled Faces in the Wild (LFW) database together with the corresponding database of attributes (LFW-attributes), show that our proposed method using LSH face soft biometrics (Soft BioHash) improves the performance of face image database search and retrieval and also outperforms the LSH hard face biometrics method (Hard BioHash).

Fusion of Hash-Based Hard and Soft Biometrics for Enhancing Face Image Database Search and Retrieval

“…Recently, the research work in [18] introduced a method to improve face image retrieval performance that replaces face soft biometrics with their related hash codes utilizing LSH (Soft BioHash). They concluded that their approach is superior to the traditional method that uses hashing for standard face features with shorter retrieval time and higher accuracy, as it was applied to the database LFW and LFW attributes.…”

“…Firstly, a facial image was detected and normalized during the data preprocessing stage; then, it was mapped and formed for the corresponding face soft biometrics. Secondly, we performed an extraction of features and hash creation for the traditional (hard) face features (Hard BioHash), in addition to hash creation for the face soft biometrics (Soft BioHash) introduced in [18]. Thirdly, the Euclidian distance was utilized for retrieving analogous pictures.…”

A Multi-Objective Semantic Segmentation Algorithm Based on Improved U-Net Networks

“…Filtering is performed by deleting the duplicate, incomplete and no research target information images in dataset B to obtain dataset C; finally, the image name is renamed to facilitate data management and processing to obtain the final dataset: dataset D. The filtering operation is the process of deduplication of massive images. In this study, the Locality Sensitive Hashing (LSH) is used for deduplication processing [23]. The LSH is a commonly used approximate nearest neighbour search algorithm, which is mainly used to solve similarity search problems in high-dimensional spaces.…”