Large-scale Face Clustering Method Research Based on Deep Learning

Wen, Zixin

doi:10.1109/mlbdbi54094.2021.00143

Cited by 3 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The DCNN model is fine tuned with a series of steps and experiments show a betterment in performance when compared with existing techniques. Wen [44] presents two deep learning based approaches for face clustering. The algorithms are based on deep subspace and graph convolutional networks to handle large scale data.…”

Section: Face Recognition and Clusteringmentioning

confidence: 99%

An Efficient Method to Accurately Cluster Large Number of High Dimensional Facial Images

Kumar¹,

Samuel

2023

IEEE Access

View full text Add to dashboard Cite

Accurately clustering large, high dimensional datasets is a challenging problem in unsupervised learning. K-means is considered to be a fast, widely used and accurate centroid based data partitioning algorithm for spherical datasets. However, its non-determinism and heavy dependence on the selection of initial cluster centers along with vulnerability to noise make it a poor candidate for clustering large datasets with high dimensionality. To overcome these, we develop a novel, nature inspired, centroid based clustering algorithm, inspired from the principles of particle physics. Our method ensures that the convergence to local optima and non-deterministic outputs are avoided. We experiment the method on large datasets of human face images. Besides, our method addresses the problem of outliers and presence of not well-separated data in these datasets. We use a deep learning model for extracting facial features into a vector of 128 dimensions. We validate the quality and accuracy of our methods using different statistical parameters like f-measure, accuracy, error rate, average in group proportion and normalized cluster size rand index. These evaluations show that our method exhibits better accuracy and quality in clustering large face image datasets, in comparison with other existing mechanisms. The strength of our algorithms is more visible as the size of the dataset grows. INDEX TERMSCentroid Based Celestial Clustering, Face clustering on large datasets, Optimization on clustering, Refined Celestial PSO Clustering

show abstract

Section: Face Recognition and Clusteringmentioning

confidence: 99%

An Efficient Method to Accurately Cluster Large Number of High Dimensional Facial Images

Kumar¹,

Samuel

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Face recognition [152] Skin lesion segmentation from dermoscopic images [153] Learning to cluster faces [154] Facial expression recognition method for identifying and recording emotion [155] Occluded face detection [156] Face anonymization with pose preservation [157] Consumer afect recognition using thermal facial ROIs [158] Criminal person recognition [159] Facial action unit detection [160] Masked face detection [161] Drunkenness face detection [162] Face detection and recognition [163] Driver drowsiness detection [164] Large-scale face clustering [165] Detection of facial action units [166] Facial expression recognition Action and activity recognition [167] Multiactor activity detection [168] One-shot video graph generation [169] Online graph depictions for tracking multiple 3D objects [170] Event stream classifcation [171] LiDAR-based 3D video object detection [172] Salient superpixel visual tracking [173] Video event recognition and elaboration from the bottom up [174] Multiobject tracking with embedded particle fow [175] Video scene graph generation [176] Video action detection [177] Multiobject tracking in autodriving [178,179] Skeleton-based action recognition [180] Video distinct object recognition by extraction of robust seeds [181] Video saliency detection [182] Close-to-real-time tracking in congested scenes Human pose detection [183] Human-object interaction detection [184] Railway driver behavior recognition system [185] Framework for object identifcation based on human local attributes…”

Section: Employmentmentioning

confidence: 99%

A State-of-the-Art Computer Vision Adopting Non-Euclidean Deep-Learning Models

Chowdhury,

Sany,

Ahamed

et al. 2023

International Journal of Intelligent Systems

View full text Add to dashboard Cite

A distance metric known as non-Euclidean distance deviates from the laws of Euclidean geometry, which is the geometry that governs most physical spaces. It is utilized when Euclidean distance is inappropriate, for as when dealing with curved surfaces or spaces with complex topologies. The ability to apply deep learning techniques to non-Euclidean domains including graphs, manifolds, and point clouds is made possible by non-Euclidean deep learning. The use of non-Euclidean deep learning is rapidly expanding to study real-world datasets that are intrinsically non-Euclidean. Over the years, numerous novel techniques have been introduced, each with its benefits and drawbacks. This paper provides a categorized archive of non-Euclidean approaches used in computer vision up to this point. It starts by outlining the context, pertinent information, and the development of the field’s history. Modern state-of-the-art methods have been described briefly and categorized by application fields. It also highlights the model’s shortcomings in tables and graphs and shows different real-world applicability. Overall, this work contributes to a collective information and performance comparison that will help enhance non-Euclidean deep-learning research and development in the future.

show abstract

“…An SSH was created by agghey and colleagues [94] in order to enable multi-scale face recognition by performing detection on feature maps of varying sizes. Anchor approaches such as FaceBoxes [95], S3FD [96], and ScaleFace [97] are also able to handle the detection of tiny objects and multi-scale faces. Salient object detection: Bringing attention to the principal object areas in an image, which are often referred to as the salient regions, is the objective of the technique of salient object detection.…”

Section: Cnn Applications For Specialized Object Detectionmentioning

confidence: 99%

An Evaluation of Deep Learning-Based Object Identification

Kolluri

Das

2022

IJRITCC

View full text Add to dashboard Cite

Identification of instances of semantic objects of a particular class, which has been heavily incorporated in people's lives through applications like autonomous driving and security monitoring, is one of the most crucial and challenging areas of computer vision. Recent developments in deep learning networks for detection have improved object detector accuracy. To provide a detailed review of the current state of object detection pipelines, we begin by analyzing the methodologies employed by classical detection models and providing the benchmark datasets used in this study. After that, we'll have a look at the one- and two-stage detectors in detail, before concluding with a summary of several object detection approaches. In addition, we provide a list of both old and new apps. It's not just a single branch of object detection that is examined. Finally, we look at how to utilize various object detection algorithms to create a system that is both efficient and effective. and identify a number of emerging patterns in order to better understand the using the most recent algorithms and doing more study.

show abstract

Large-scale Face Clustering Method Research Based on Deep Learning

Cited by 3 publications

References 23 publications

An Efficient Method to Accurately Cluster Large Number of High Dimensional Facial Images

An Efficient Method to Accurately Cluster Large Number of High Dimensional Facial Images

A State-of-the-Art Computer Vision Adopting Non-Euclidean Deep-Learning Models

An Evaluation of Deep Learning-Based Object Identification

Contact Info

Product

Resources

About