Missing Child Identification System Using Deep Learning and Multiclass SVM

Chandran, Pournami S; Byju, N B; Deepak, R.; Nishakumari, K N; Devanand, P; Sasi, P M

doi:10.1109/raics.2018.8635054

Cited by 26 publications

(13 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bhanumathi et al proposed a deep learning-based system to find missing people. The system utilized Convolutional Neural Networks (CNNs) to find missing people (Chandran et al, 2018). Jhanani et al introduced another application based on TensorFlow to detect criminals and missing people (Jhanani et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Suhail: A Deep Learning-Based System for Identifying Missing People

Aljohani¹,

Alshehri²,

Alghamdi³

et al. 2023

CIS

View full text Add to dashboard Cite

Many people become missing in Saudi Arabia every day, including children, young people, and the mentally ill as well as the elderly with Alzheimer’s. There are many missing people cases that are still unsolved. In Saudi Arabia, people use social media platforms such as Twitter to report missing people cases. The application of deep learning has been successful in a wide range of fields including computer vision and machine vision. In particular, face recognition techniques are effective in saving time and effort, especially when searching for missing individuals. Hence, the goal of this research is to solve this issue by developing a deep learning-based system for identifying missing individuals. This paper introduces a new system called Suhail. The system has been implemented and developed using Android Studio and open-source libraries such as TensorFlow. First, users or governments can report missing persons by uploading photos. Updates and information will then be shared with the rest of the system’s users (volunteers). Once a volunteer discovers a suspect, they scan their face using camera. Then, our application uses face recognition techniques to compare the suspect's photo with photos from the repository. Finally, once a comparison is found, our application contacts the suspect’s family, informs them of his location and then notifies the police that a missing person has been located. By using our application and face recognition systems, we help families and police locate and reach a missing person which saves time and effort. In this study, 759 participants were enrolled to evaluate the performance of the Suhail system. Engagement, aesthetics, and functionality are used to evaluate the user experience. The results of the experiment show that users enjoy the new features of the application and that the system is simple to use. Moreover, the system would help governments and individuals identify missing people faster.

show abstract

Section: Related Workmentioning

confidence: 99%

Suhail: A Deep Learning-Based System for Identifying Missing People

Aljohani¹,

Alshehri²,

Alghamdi³

et al. 2023

CIS

View full text Add to dashboard Cite

show abstract

“…Chandran et al used CNN and Multi-Class Support Vector Machine (SVM) to recognize children's faces [37]. The architecture created is based on VGG-Face.…”

Section: Hu Et Al Used 3 Convolutional Neural Network Arranged In Par...mentioning

confidence: 99%

A Survey of Face Recognition based on Convolutional Neural Network

Saragih

2022

Indonesian J. of Inf. Syst.

View full text Add to dashboard Cite

Face recognition is one of the interesting research topics in the field of computer vision. In recent years, deep learning methods, especially the Convolutional Neural Network, have progressed. One of the successes of CNN is in face recognition. Face recognition by computer is a technique done so that the computer can automatically recognize faces in an image. Various researchers have conducted related research on facial recognition. This survey presents researches related to face recognition based on Convolutional Neural Network that has been conducted. The studies used are studies that have been published in the last five years. It was performed to determine the renewal that emerged in face recognition based on Convolutional Neural Network. The basic theory of the Convolutional Neural Network, face recognition, and description of the database used in various researches are also discussed. Hopefully, this survey can provide additional knowledge regarding face recognition based on the Convolutional Neural Network.

show abstract

“…Pournami et al [28] have offered deep learning and multiclass SVM algorithm to recognize face. Here, preprocessing was performed to increase the accuracy of the face recognition where image resizing was performed.…”

Section: Related Workmentioning

confidence: 99%

Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature

Kumar¹,

Pavani²

2022

Artificial Intelligence Annual Volume 2022

View full text Add to dashboard Cite

Face recognition across aging emerges as a significant area among researchers due to its applications such as law enforcement, security. However, matching human faces with different age gaps is still bottleneck due to face appearance variations caused by aging process. In regard to mitigate such inconsistency, this chapter offers five sequential processes that are Image Quality Evaluation (IQE), Preprocessing, Pose Normalization, Feature Extraction and Fusion, and Feature Recognition and Retrieval. Primarily, our method performs IQE process in order to evaluate the quality of image and thus increases the performance of our Age Invariant Face Recognition (AIFR). In preprocessing, we carried out two processes that are Illumination Normalization and Noise Removal that have resulted in high accuracy in face recognition. Feature extraction adopts two descriptors such as Convolutional Neural Network (CNN) and Scale Invariant Heat Kernel Signature (SIHKS). CNN extracts texture feature, and SIHKS extracts shape and demographic features. These features plays vital role in improving accuracy of AIFR and retrieval. Feature fusion is established using Canonical Correlation Analysis (CCA) algorithm. Our work utilizes Support Vector Machine (SVM) to recognize and retrieve images. We implement these processes in FG-NET database using MATLAB2017b tool. At last, we validate performance of our work using seven performance metrics that are Accuracy, Recall, Rank-1 Score, Precision, F-Score, Recognition rate and computation time.

show abstract

Missing Child Identification System Using Deep Learning and Multiclass SVM

Cited by 26 publications

References 4 publications

Suhail: A Deep Learning-Based System for Identifying Missing People

Suhail: A Deep Learning-Based System for Identifying Missing People

A Survey of Face Recognition based on Convolutional Neural Network

Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature

Contact Info

Product

Resources

About