Computer vision has become a highly evolving field in recent years, dealing with methods for obtaining, processing, examining, and understanding digital images. Human face recognition in computer vision has a vital role to play in security and surveillance, and the mechanisms for increasing the security levels are strengthening day by day. The existing human face recognition system has been enhanced by introducing an anti-spoofing mechanism which will help to stop a nefarious person to intentionally get around with the system. This article focuses on an approach to detect a human face using texture analysis which includes computing a Histogram of Gradients (HOG) over a region of the face and then uses Support Vector Machines (SVMs) to recognize a face. A blink detection mechanism used in this article ensures the liveliness of the person, making the system more reliable. A Raspberry Pi module is used in implementing the work involved in this paper and the programming is done in Python using libraries like OpenCV and NumPy. This model can achieve a maximum accuracy of 92.68% and achieves optimal results during the afternoon, taking a total of 9.89 seconds for face recognition and blink detection. © 2021 IEEE.