Unconstrained face detection: a Deep learning and Machine learning combined approach

Sawat, Dattatray D.; Hegadi, Ravindra S.

doi:10.1007/s40012-016-0149-1

Cited by 24 publications

(4 citation statements)

References 9 publications

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“…By adjusting and optimizing existing deep learning models such as VGG, ResNet, Inception, and combining techniques like data augmentation and transfer learning, the performance and effectiveness of CNNs in image processing tasks can be further enhanced. In summary, CNNs, with their advantages in feature extraction and pattern recognition, have become important technologies in the field of image processing, achieving widespread applications and success in tasks such as image classification and object detection [3].…”

Section: Principles and Techniques Of Cnn In Image Processingmentioning

confidence: 99%

Research on Complex Data Mining Analysis and Pattern Recognition Based on Deep Learning

Lian,

Chen

2024

JCEIM

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This paper systematically investigates and discusses the application of deep learning in complex data mining analysis and pattern recognition. Firstly, it introduces the basic concepts of deep learning and commonly used models, including artificial neural networks, convolutional neural networks, and recurrent neural networks. Then, it elaborates on the application methods and techniques of deep learning in mining different types of complex data (such as images, text, time series, etc.), and explores the latest research progress in the field of pattern recognition. Furthermore, it analyzes the challenges faced by deep learning in practical applications, such as data scarcity and model generalization capabilities, and proposes future development trends and research directions. Finally, it summarizes the research content and significance of this paper, emphasizing the importance and application prospects of deep learning in the field of complex data mining and pattern recognition.

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Section: Principles and Techniques Of Cnn In Image Processingmentioning

confidence: 99%

Research on Complex Data Mining Analysis and Pattern Recognition Based on Deep Learning

Lian,

Chen

2024

JCEIM

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“…Qin et al proposed a joint training method to optimize the CNN cascade training process with the same goal, which is to detect faces [23]. Sawat and Hegadi proposed a method for detecting face by combining CNN and Cubic Support Vector Machine [24]. Face Alignment The stage after detecting the face is to align the face.…”

Section: Face Detectionmentioning

confidence: 99%

A Survey of Face Recognition based on Convolutional Neural Network

Saragih

2022

Indonesian J. of Inf. Syst.

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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.

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“…Recent advances in deep learning methods have contributed to significant performance improvements in a wide range of computer vision applications. They have been particularly successful for face detection problems where modern deep CNN models show a significant accuracy improvement in comparison to traditional approaches based on hand-crafted features [22][23][24][25][26][27][28][29][39][40][41][42][43][44][45]. Consequently, these deep learning methods have become the state-of-the-art for face detection.…”

Section: Related Workmentioning

confidence: 99%

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Chaves

Fidalgo

Alegre

et al. 2020

Sensors

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Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require large computation power and processing time. In this work, we evaluate the speed–accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed–accuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113, which is very promising for the forensic field.

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Unconstrained face detection: a Deep learning and Machine learning combined approach

Cited by 24 publications

References 9 publications

Research on Complex Data Mining Analysis and Pattern Recognition Based on Deep Learning

Research on Complex Data Mining Analysis and Pattern Recognition Based on Deep Learning

A Survey of Face Recognition based on Convolutional Neural Network

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

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