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Electronics
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Intelligent Face Recognition and Multiple Applications
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Intelligent Face Recognition and Multiple Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 7129

Special Issue Editors

Dr. Hilario Gómez Moreno

E-Mail Website
Guest Editor
Polytechnic School, Signal Theory and Communications Department, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain
Interests: pattern recognition; artificial intelligence and forensics applications
Prof. Dr. Sergio Lafuente-Arroyo

E-Mail Website
Guest Editor
Polytechnic School, Signal Theory and Communications Department, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain
Interests: pattern recognition; indoor navigation; robotics

Special Issue Information

Dear Colleagues,

The human face has become one of the most widely used and accepted biometric recognition methods. The field of facial recognition has gained the attention of many scientists and has therefore become a standard reference in the area of human recognition. It has also become one of the most deeply studied areas in computer vision and a great number of intelligent face recognition techniques and applications have been developed. These systems present a wide range of possibilities and applications, such as security monitoring, automated surveillance systems, and identification of victims and missing persons, etc.

Internationally, large cities are taking advantage of the potential of these systems to implement surveillance measures that make it possible to find any citizen in a matter of seconds. In other cases, they are working on the implementation of these automatic facial recognition systems by conducting pilot tests with different models and purposes.

However, despite the exponential progress made in recent years in face detection and recognition, there is still a lot of work to be done. We also have the inherent difficulties in image processing, such as image resolution, illumination, face position, temporal lapse, etc., and this field has recently been facing the added problem of GANs and diffusion models that generate realistic (and totally false) human faces that are increasingly indistinguishable from real ones.

In this context, the MDPI journal Electronics is publishing a Special Issue entitled "Intelligent Face Recognition and Multiple Applications" to bring together recent work in this field. This open access journal has a high visibility and a growing JCR impact factor.

Specifically, this Special Issue aims to compile a diverse and complementary set of articles showcasing new developments in and applications of facial recognition to address new and challenging problems by proposing advanced solutions and systems for face processing and analysis. 

Dr. Hilario Gómez Moreno
Prof. Dr. Sergio Lafuente-Arroyo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • face recognition methods
  • face and features detection
  • facial features codification
  • pre-processing methods
  • biometrics
  • face recognition applications
  • facial deepfakes detection
  • facial identity protection, watermarking
  • facial sentiment analysis
  • automated surveillance systems
  • identification of missing persons

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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12 pages, 1402 KiB  
Article
Face Detection Based on DF-Net
by Qijian Tang, Yanfei Li, Yinhe Cai, Xiang Peng and Xiaoli Liu
Electronics 2023, 12(19), 4021; https://doi.org/10.3390/electronics12194021 - 24 Sep 2023
Viewed by 1645
Abstract
Face data have found increasingly widespread applications in daily life. To efficiently and accurately extract face information from input images, this paper presents a DF-Net-based face detection approach. A lightweight facial feature extraction neural network based on the MobileNet-v2 architecture is designed and [...] Read more.
Face data have found increasingly widespread applications in daily life. To efficiently and accurately extract face information from input images, this paper presents a DF-Net-based face detection approach. A lightweight facial feature extraction neural network based on the MobileNet-v2 architecture is designed and implemented. By incorporating multi-scale feature fusion and spatial pyramid modules, the system achieves face localization and extraction across multiple scales. The proposed network is trained on the open-source face detection dataset WiderFace. The hyperparameters such as bottleneck coefficients and quality factors are discussed. Comparative experiments with other commonly used networks are carried out in terms of network model size, processing speed, and network extraction accuracy. Experimental results affirm the efficacy and robustness of this method, especially in challenging facial poses. Full article
(This article belongs to the Special Issue Intelligent Face Recognition and Multiple Applications)
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17 pages, 599 KiB  
Article
Facial Expression Recognition in the Wild for Low-Resolution Images Using Voting Residual Network
by José L. Gómez-Sirvent, Francisco López de la Rosa, María T. López and Antonio Fernández-Caballero
Electronics 2023, 12(18), 3837; https://doi.org/10.3390/electronics12183837 - 11 Sep 2023
Cited by 6 | Viewed by 2204
Abstract
Facial expression recognition (FER) in the wild has attracted much attention in recent years due to its wide range of applications. Most current approaches use deep learning models trained on relatively large images, which significantly reduces their accuracy when they have to infer [...] Read more.
Facial expression recognition (FER) in the wild has attracted much attention in recent years due to its wide range of applications. Most current approaches use deep learning models trained on relatively large images, which significantly reduces their accuracy when they have to infer low-resolution images. In this paper, a residual voting network is proposed for the classification of low-resolution facial expression images. Specifically, the network consists of a modified ResNet-18, which divides each sample into multiple overlapping crops, makes a prediction of the class to which each of the crops belongs, and by soft-voting the predictions of all the crops, the network determines the class of the sample. A novel aspect of this work is that the image splitting is not performed before entering the network, but at an intermediate point in the network, which significantly reduces the resource consumption. The proposed approach was evaluated on two popular benchmark datasets (AffectNet and RAF-DB) by scaling the images to a network input size of 48 × 48. The proposed model reported an accuracy of 63.06% on AffectNet and 85.69% on RAF-DB with seven classes in both cases, which are values comparable to those provided by other current approaches using much larger images. Full article
(This article belongs to the Special Issue Intelligent Face Recognition and Multiple Applications)
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14 pages, 3182 KiB  
Article
Locality-Sensitive Hashing of Soft Biometrics for Efficient Face Image Database Search and Retrieval
by Ameerah Abdullah Alshahrani and Emad Sami Jaha
Electronics 2023, 12(6), 1360; https://doi.org/10.3390/electronics12061360 - 13 Mar 2023
Cited by 3 | Viewed by 2623
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 [...] Read more.
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). Full article
(This article belongs to the Special Issue Intelligent Face Recognition and Multiple Applications)
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