Face Recognition is a challenging task for recognizing and detecting the identity of an individual. Although, plethora of work has already been done in the field of pattern recognition still there has been lot which has not been addressed in any of the literature. In the current research, we have presented a comparative analysis using three popularly known techniques for face recognition namely, Principal Components Analysis (PCA) using Eigen Faces, Hidden Markov Model (HMM) using Singular Value Decomposition, and Artificial Neural Network (ANN) using Gabor filters. These techniques are implemented and evaluated using various measuring metrics such as false acceptance, false recognition rate, and so on. We used ORL and Yale Face dataset to test the robustness of implemented algorithms. Results show that ANN model for face recognition outperforms the other two techniques by achieving more accurate results and shows the highest recognition rate of 97.49% on ORL database. Moreover, it is also observed that ANN model shows the minimum error count of about 2.502% on ORL database while it is 3.5% on Yale Face dataset. To evaluate further, the implemented techniques are compared with best known techniques in class implemented by various researchers.

中文翻译：

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主成分分析，隐马尔可夫模型和人工神经网络启发技术来识别人脸

人脸识别是识别和检测个人身份的一项艰巨任务。尽管在模式识别领域已经做了大量的工作，但是仍然有很多文献没有解决。在当前的研究中，我们已经提出了使用三种广为人知的面部识别技术的比较分析，即使用特征脸的主成分分析（PCA），使用奇异值分解的隐马尔可夫模型（HMM）和使用人工神经网络的人工神经网络（ANN）。 Gabor过滤器。使用各种测量指标（例如错误接受，错误识别率等）来实现和评估这些技术。我们使用ORL和Yale Face数据集来测试已实现算法的鲁棒性。结果表明，用于人脸识别的ANN模型取得了更准确的结果，优于其他两种技术，并且在ORL数据库上显示出最高的97.49％的识别率。此外，还可以观察到，ANN模型在ORL数据库上显示的最小错误计数约为2.502％，而在Yale Face数据集上的最小错误计数为3.5％。为了进一步评估，将实施的技术与各种研究人员实施的同类最佳已知技术进行比较。