Boosting the Face Recognition Performance of Ensemble Based LDA for Pose, Non-uniform Illuminations, and Low-Resolution Images

doi:10.3837/tiis.2019.06.021

Cited by 5 publications

(2 citation statements)

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“…An experiment was evaluated in which the collected player images were recognized and tested on state-of-the-art face recognition algorithms, including PCA [58], AdaBoost-LDA [43], CNN [59], and Capsule Network used for character recognition [60], in order to assess the robustness of the face recognition algorithm used in this model. The state of certain algorithms caused images of faces to be transformed to 128 × 128 pixels.…”

Section: Comparison With Cutting-edge Algorithmsmentioning

confidence: 99%

Automatic Player Face Detection and Recognition for Players in Cricket Games

Haq,

Sethi,

Ahmad

et al. 2024

IEEE Access

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In this Paper, we create an augmented reality cricket broadcasting application that uses player recognition and automatic detection during play to display player personal data. The system uses AdaBoost to detect player and player face and use PAL based face recognition model to recognize the faces of players on the field. The system is trained on a large dataset of cricket game footage and achieves high accuracy in detecting and recognizing players' faces even with several conditions such as occlusion, non-uniform illumination and pose variation. The system has the potential to enhance the viewing experience of cricket games by providing real-time player identification and statistics. The system can also be used in other sports to provide similar benefits. The paper discusses the system's methodology, results, and implications for the future of sports broadcasting. Overall, the system provides a promising solution for automatic player face detection and recognition in sports broadcasting.

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Section: Comparison With Cutting-edge Algorithmsmentioning

confidence: 99%

Automatic Player Face Detection and Recognition for Players in Cricket Games

Haq,

Sethi,

Ahmad

et al. 2024

IEEE Access

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“…This technique was robust to dimensionality reduction and retaining salient information. According to [2][3] strong illumination and lighting conditions are sensitive for effective feature description hence using robust feature representation compensate for the lost spatial features. According to work proposed by [4]application of Min-Max and Z-score normalization schemes along with fusion methods results in improved recognition rates.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Robust 2D-PCA and 2D-LDA Feature Descriptors on SVM

Gupta¹

2020

IJMTST

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Facialexpressions are the most intuitive way of communicating non-verbal messages. This type of communication provides effective response and feedback from the speaker to listener and vice-versa. In this paper robust macro facial expression recognition techniques are presented. 2D-PCA and 2D-LDA are robust geometric feature descriptors presented in this paper capable of cancelling noise and extracting maximum spatial features from image samples with unstable illumination condition which leads to correct classification of results. Experiments are carried out separately on both feature descriptors using Cohn Kanade (CK+) dataset, MMI dataset, and Japanese Female Facial Expressions (JAFFE) database for analysis. The experimental results are evaluated and their performance compared using Support vector machine (SVM) classifier with three kernels; linear, polynomial and radial basis function (RBF). The results from the proposed methods are also compared with existing novel methods and it is found out that the results from the proposed methods perform significantly well.

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