Addressing facial dynamics using k-medoids cohort selection algorithm for face recognition

Garain, Jogendra; Kumar, Rajneesh; Kisku, Dakshina Ranjan; Sanyal, Goutam

doi:10.1007/s11042-018-7132-9

Cited by 6 publications

(4 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2019, Garain et al [18] have designed a new K-medoids Cohort Selection (KMCS) for choosing a reference group of non-matched templates regarding the considerable subjects. Generally, KMCS was used for clustering the entire scores of cohort subjects.…”

Section: Existing Workmentioning

confidence: 99%

See 1 more Smart Citation

A Smart Face Recognition and Verification using Optimal Spatial and Spectral Feature Selection with Adaptive Multiscale Mobilenet

Shivaprakash,

Rajashekararadhya

2023

AAIML

View full text Add to dashboard Cite

Face recognition is complicated work, which is highly demanding while processing the interclass similarities and intra-class differentiations in the acquired images in a wider range. However, the identification accuracy can be enhanced at some level through managing the system with non-matched templates. In the reality, face recognition is complicated owing to differentiations in the pose, background, illumination, and so on. On the other hand, the type of face recognition technique is recently dependent on machine learning-based facial features while avoiding the practical experiences in hand-craft features. Recent world, the face recognition technique using deep learning strategy is capable of learning efficient face features for getting a highly extraordinary efficiency. The face identification techniques use various conventional appearance methods, which are further applied for testing the efficiency via applying the benchmark facial images. The facial images gathered from the standard digital media are often noticed as problems regarding occlusion, lightning, and position conditions along with camera angle. The occluded images can be observed with the alignment, facial expressions, and human pose along with the camera axis. Thus, more attention must be taken care during the acquiring the facial images along with the coverage of the background. Hence, it is necessary for considering the precise pre-processing steps along with the necessary steps for recognizing the face. In this research, a new face recognition model is investigated utilizing deep learning techniques. Initially, the images are gathered from the standard resources. Next, they are pre-processed to increase the quality of images for further processes. Then, the spatial and spectral feature extraction is performed, where the spatial features are extracted by DeepLabV3, and the spectral features are extracted using Discrete Wavelet Transform (DWT) and the Discrete Cosine Transform (DCT). Next, the optimal spectral features and optimal spatial features are attained by using a new hybrid heuristic 1407

show abstract

Section: Existing Workmentioning

confidence: 99%

“…Some of the existing face recognition approaches are reviewed in TABLE 1. KMCS [18] achieves a higher convergence time and offers faster computation, and has less expensive. It is more sensitive to noise.…”

Section: Problem Specificationmentioning

confidence: 99%

A Smart Face Recognition and Verification using Optimal Spatial and Spectral Feature Selection with Adaptive Multiscale Mobilenet

Shivaprakash,

Rajashekararadhya

2023

AAIML

View full text Add to dashboard Cite

show abstract

“…Though, face recognition is still a difficult and demanding topic when it comes to unconstrained environments, such as those with changeable illumination and various poses [7,8], which have been the subject of many successful research results in recent years. In our lifetime, we have learned to remember hundreds of faces and can recognize famous faces with a quick glance, even after years of separation.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Classical and Modern Face Recognition Techniques

ShalimaSulthana

Nagaraju

2021

IJSRCSEIT

View full text Add to dashboard Cite

During the previous few centuries, facial recognition systems have become a popular research topic. On account of its extraordinary success and vast social applications; it has attracted significant study attention from a wide range of disciplines in the last five years - including “computer-vision”, “artificial-intelligence”, and “machine-learning”. As with most face recognition systems, the fundamental goal involves recognizing a person's identity by means of images, video, data streams, and context information. As a result of our research; we've outlined some of the most important applications, difficulties, and trends in scientific and social domains. This research, the primary goal is to summarize modern facial recognition algorithms and to gain a general perceptive of how these techniques act on diverse datasets. Aside from that, we also explore some significant problems like illumination variation, position, aging, occlusion, cosmetics, scale, and background are some of the primary challenges we examine. In addition to traditional face recognition approaches, the most recent research topics such as sparse models, deep learning, and fuzzy set theory are examined in depth. There's also a quick discussion of basic techniques, as well as a more in-depth. As a final point, this research explores the future of facial recognition technologies and their possible importance in the emerging digital society.

show abstract

“…To solve this problem, Johnson et al [27] introduced a k-medoids clustering approach to identify the minimum number of sequences necessary to represent each coding sequence in the final probe set. Garain et al [28] proposed a cohort selection method called k-medoids cohort selection (KMCS) to select a reference set of non-matched templates which are almost appropriate to the respective subjects. There are many random factors during the process of k-medoids algorithm, so it is essential to avoid the randomness of clustering process.…”

Section: Introductionmentioning

confidence: 99%

Time Series Clustering Model based on DTW for Classifying Car Parks

Zhang

2020

Algorithms

View full text Add to dashboard Cite

An increasing number of automobiles have led to a serious shortage of parking spaces and a serious imbalance of parking supply and demand. The best way to solve these problems is to achieve the reasonable planning and classify management of car parks, guide the intelligent parking, and then promote its marketization and industrialization. Therefore, we aim to adopt clustering method to classify car parks. Owing to the time series characteristics of car park data, a time series clustering framework, including preprocessing, distance measurement, clustering and evaluation, is first developed for classifying car parks. Then, in view of the randomness of existing clustering models, a new time series clustering model based on dynamic time warping (DTW) is proposed, which contains distance radius calculation, obtaining density of the neighbor area, k centers initialization, and clustering. Finally, some UCR datasets and data of 27 car parks are employed to evaluate the performance of the models and results show that the proposed model performs obviously better results than those clustering models based on Euclidean distance (ED) and traditional clustering models based on DTW.

show abstract

Addressing facial dynamics using k-medoids cohort selection algorithm for face recognition

Cited by 6 publications

References 48 publications

A Smart Face Recognition and Verification using Optimal Spatial and Spectral Feature Selection with Adaptive Multiscale Mobilenet

A Smart Face Recognition and Verification using Optimal Spatial and Spectral Feature Selection with Adaptive Multiscale Mobilenet

A Survey on Classical and Modern Face Recognition Techniques

Time Series Clustering Model based on DTW for Classifying Car Parks

Contact Info

Product

Resources

About