Face Image Retrieval of Efficient Sparse Code words and Multiple Attribute in Binning Image

Suchitra, S.

doi:10.1590/1678-4324-2017160480

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Such multiple attributes help to identify and differentiate similar images in the database and index/rank the results depending on their similarity. Many research efforts have concentrated on retrieval systems for multiple attributes of images, but the desired high performance is still a challenging task [9]. Moreover, the retrieval of encrypted image data is still difficult because it must be performed rapidly while protecting privacy [10].…”

Section: Introductionmentioning

confidence: 99%

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

Alshahrani

Jaha

2023

Electronics

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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).

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Section: Introductionmentioning

confidence: 99%

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

Alshahrani

Jaha

2023

Electronics

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“…In general, a retrieval system, which depends on extracting multiple attributes, can make it easier to distinguish between comparable images in a database and index or rank them according to their similarity. Even though many researchers focus on different and multiple image attributes, achieving the requisite high performance still represents a challenging issue [6].…”

Section: Introductionmentioning

confidence: 99%

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

Alshahrani,

Jaha,

Alowidi

2023

CMC

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The utilization of digital picture search and retrieval has grown substantially in numerous fields for different purposes during the last decade, owing to the continuing advances in image processing and computer vision approaches. In multiple real-life applications, for example, social media, content-based face picture retrieval is a well-invested technique for large-scale databases, where there is a significant necessity for reliable retrieval capabilities enabling quick search in a vast number of pictures. Humans widely employ faces for recognizing and identifying people. Thus, face recognition through formal or personal pictures is increasingly used in various reallife applications, such as helping crime investigators retrieve matching images from face image databases to identify victims and criminals. However, such face image retrieval becomes more challenging in large-scale databases, where traditional vision-based face analysis requires ample additional storage space than the raw face images already occupied to store extracted lengthy feature vectors and takes much longer to process and match thousands of face images. This work mainly contributes to enhancing face image retrieval performance in large-scale databases using hash codes inferred by locality-sensitive hashing (LSH) for facial hard and soft biometrics as (Hard BioHash) and (Soft BioHash), respectively, to be used as a search input for retrieving the top-k matching faces. Moreover, we propose the multi-biometric score-level fusion of both face hard and soft BioHashes (Hard-Soft BioHash Fusion) for further augmented face image retrieval. The experimental outcomes applied on the Labeled Faces in the Wild (LFW) dataset and the related attributes dataset (LFW-attributes), demonstrate that the retrieval performance of the suggested fusion approach (Hard-Soft BioHash Fusion) significantly improved the retrieval performance compared to solely using Hard BioHash or Soft BioHash in isolation, where the suggested method provides an augmented accuracy of 87% when executed on 1000 specimens and 77% on 5743 samples. These results remarkably outperform the results of the Hard BioHash method by (50% on the 1000 samples and 30% on the 5743 samples), and the Soft BioHash method by (78% on the 1000 samples and 63% on the 5743 samples).

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

Hasnat¹,

Halder²,

Bhattacharjee

et al. 2019

Communications in Computer and Information Science

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Face Image Retrieval of Efficient Sparse Code words and Multiple Attribute in Binning Image

Cited by 4 publications

References 21 publications

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

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

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

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

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