Clustering algorithms for face recognition based on client-server architecture

Oravec, Milos; Sopiak, Dominik; Jirka, Vojtech; Pavlovičová, Jarmila; Budiak, Mark

doi:10.1109/iwssip.2015.7314221

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…In particular, the best results are got using the propounded face reconstruction approach. [64] 75.00 C-means [65] 72.08 SIFT [33] 74…”

Section: Discussionmentioning

confidence: 99%

Comparative Study Based on De-Occlusion and Reconstruction of Face Images in Degraded Conditions

Ouannes¹,

Khalifa²,

Amara³

2021

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In the recent years, the face recognition task has attracted the attention of researchers due to its efficiency in several domains such as surveillance and access control. Unfortunately, there are multiple challenges that decrease the performance of face recognition. Partial occlusion is the most challenging one since it often causes a great lack of information. The main purpose of this paper is to prove that facial reconstruction improves the results of facial recognition compared to de-occlusion and full-face recognition in the presence of occlusion. Our objective is to achieve occluded-face recognition, de-occluded-face recognition, and reconstructed-face recognition. Regarding face reconstruction, we introduce two different methods based on Laplacian pyramid blending and CycleGANs. In order to validate our work, we perform two different feature extraction techniques: hand-crafted features and learned features exploiting the final layers of a pre-trained deep architecture model. The experimental results on the EURECOM Kinect Face Dataset (EKFD) and the IST-EURECOM Light Field Face Database (IST-EURECOM LFFD) show that the proposed face reconstruction approach, compared with the face de-occlusion and occluded-face recognition ones, clearly improves the face recognition task. Our method boosts the classification performance in comparison with the state-of-the-art methods, achieving 94.66% on EKFD and 72.35% on IST-EURECOM LFFD.

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“…In particular, the best results are got using the propounded face reconstruction approach. [64] 75.00 C-means [65] 72.08 SIFT [33] 74…”

Section: Discussionmentioning

confidence: 99%

Comparative Study Based on De-Occlusion and Reconstruction of Face Images in Degraded Conditions

Ouannes¹,

Khalifa²,

Amara³

2021

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“…Clustering includes different algorithms namely: k-means, c-means, Rank-Order, Co-Clustering. Kmeans clustering groups data vectors into a pre-defined number of cluster based on Euclidean distance; c-means clustering is a data clustering algorithm in which each data point is associated with cluster through a membership degree [12]; Rank -Order clustering is a form of agglomerative hierarchical clustering using nearest neighbour based distance measure [13]; Co-clustering method automatically select the class independent salient features from still image. 1) K-Means Clustering Algorithm: k-means algorithms proved their usefulness in the area of image analysis, segmentation process.…”

Section: Fig 4 Fisher Faces C Clustering Algorithmsmentioning

confidence: 99%

A Novel Study on Face Recognition Algorithms and its Challenges

Acha¹

2018

IJRASET

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Face recognition system is an application of computer vision and Image processing which is capable of performing two major tasks of identifying and verifying a person from an image or a video database. The human face is a complicated multidimensional model and it needs a sturdy recognition technique to recognize a human face. The face recognition accuracy is depends on two procedure which are human face detection and a feature extraction method. Many of face recognition feature extraction techniques are used to recognize the face but it needs more enhancements acquire to get optimum outcome. This paper majorly concentrates on comparative study between PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), Clustering and SVM (Support Vector Machine) algorithms.

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