<![CDATA[Currently, the incidence of theft crimes by breaking into house doors is increasing. The importance of a security system is to prevent unknown parties from stealing or violating privacy without the owner's consent. Biometric technology can create a strong security system, by utilizing the biological characteristics that every human has, such as fingerprints, facial detection, eye retina and voice. One of the biometrics that is considered strong when building a security system is facial recognition. This research uses the Haar Cascade Classifier algorithm supported by OpenCV to increase the accuracy of facial identification based on facial structure and eye feature extraction. The training and testing process is carried out directly (real time) using the OV2640 camera and dataset. The designed prototype consists of an ESP32 CAM microcontroller, relay, and door lock solenoid which is integrated with telegram as notification. Based on the test results, it shows that the accuracy of matching facial images using the Haar Cascade Classifier algorithm which matches the database is 80%. Apart from that, the results of testing the distance of the face to the camera, variations in light, position and facial expressions that can be recognized with the ESP32 CAM camera greatly influence the face detection process. In this case, the effective distance is 25-55 cm in light conditions with a light intensity of 83-450 lux, and the face is facing forward. Apart from that, the system is also able to differentiate between human face objects and non-human face objects. The tool's performance from detection to sending unrecognized image data to Telegram took an average of 6.4 ms. From the test results, it is also known that the perfection of facial appearance that can be recognized with the ESP32 CAM camera has a great influence on the face detection proce]]>
