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Attendance management is an essential component in educational institutions, companies, and organizations to monitor the presence and punctuality of participants. Traditional attendance systems, such as manual signatures or identification cards, are prone to various issues including human error, time inefficiency, and identity fraud. To address these challenges, this study aims to develop a smart attendance system using facial recognition technology based on Python and the OpenCV library. The system is designed to automatically detect and recognize faces in real time using a webcam or camera module. It employs computer vision techniques to capture facial images, extract unique features, and match them against a stored database of registered participants. Once the face is verified, the system records the attendance along with a timestamp, ensuring data accuracy and security. The development process involved several stages, including image acquisition, preprocessing, feature extraction, and classification. OpenCV was utilized for image processing tasks, while Python provided the programming framework to integrate all components. To enhance recognition accuracy, the system applied techniques such as histogram equalization for lighting normalization and Haar Cascade classifiers for initial face detection. An experimental evaluation was conducted under various conditions, including different lighting environments and facial orientations. The results demonstrated that the system achieved an accuracy rate of 96% under normal lighting conditions, with only a small decrease in performance under dim or uneven lighting. These findings indicate that the system is reliable for practical applications, especially in controlled environments. Conclusion: The Python-based facial recognition attendance system offers a more efficient, secure, and accurate alternative to conventional attendance methods. Future improvements may include the integration of deep learning models to enhance recognition robustness in diverse real-world scenarios.

Attendance management is an essential component in educational institutions, companies, and organizations to monitor the presence and punctuality of participants. Traditional attendance systems, such as manual signatures or identification cards, are prone to various issues including human error, time inefficiency, and identity fraud. To address these challenges, this study aims to develop a smart attendance system using facial recognition technology based on Python and the OpenCV library. The system is designed to automatically detect and recognize faces in real time using a webcam or camera module. It employs computer vision techniques to capture facial images, extract unique features, and match them against a stored database of registered participants. Once the face is verified, the system records the attendance along with a timestamp, ensuring data accuracy and security. The development process involved several stages, including image acquisition, preprocessing, feature extraction, and classification. OpenCV was utilized for image processing tasks, while Python provided the programming framework to integrate all components. To enhance recognition accuracy, the system applied techniques such as histogram equalization for lighting normalization and Haar Cascade classifiers for initial face detection. An experimental evaluation was conducted under various conditions, including different lighting environments and facial orientations. The results demonstrated that the system achieved an accuracy rate of 96% under normal lighting conditions, with only a small decrease in performance under dim or uneven lighting. These findings indicate that the system is reliable for practical applications, especially in controlled environments. Conclusion: The Python-based facial recognition attendance system offers a more efficient, secure, and accurate alternative to conventional attendance methods. Future improvements may include the integration of deep learning models to enhance recognition robustness in diverse real-world scenarios.

The integration of robotics in education is transforming the way students engage with STEM subjects. This paper explores the effectiveness of robotics in developing problem-solving, programming, and critical thinking skills in young learners. Case studies from various educational institutions demonstrate the positive impact of robotics-based learning in fostering innovation and creativity among children.

The integration of robotics in education is transforming the way students engage with STEM subjects. This paper explores the effectiveness of robotics in developing problem-solving, programming, and critical thinking skills in young learners. Case studies from various educational institutions demonstrate the positive impact of robotics-based learning in fostering innovation and creativity among children.

During this pandemic, most of people’s activities are carried out through digital media. Both learning and working processes are using the video-conference platform, a platform deemed effective to facilitate the needs of distance communication. One of the limitations of using video-conference lies in difficulty in understanding emotional conditions based on solely camera video. Hence, speakers generally do not know their interlocutors’ feelings related to the materials being presented. Grounded on this issue, we examined a facial expressions-based emotion recognition tool. Micro expression is one of the micro-languages of communication. Machine learning model developed in this study was Deep Learning with Convolutional Neural Network (CNN). The library that was used was Keras, this was used to recognize micro expression pattern. Additionally, OpenCV was also used for the general face recognition process. Both libraries were operated using Python programming language. The result of the micro expression test involving thirty participants detected three types of facial expression, namely joy, sadness, and anger expression. However, face recognition applied in the present study still needed some improvements, especially for anger and sadness expression. With regard to joy expression, 89% of the expression were recognized. Based on the recorded data, it is necessary to improve the recordings criteria to obtain a clearer expression.

During this pandemic, most of people’s activities are carried out through digital media. Both learning and working processes are using the video-conference platform, a platform deemed effective to facilitate the needs of distance communication. One of the limitations of using video-conference lies in difficulty in understanding emotional conditions based on solely camera video. Hence, speakers generally do not know their interlocutors’ feelings related to the materials being presented. Grounded on this issue, we examined a facial expressions-based emotion recognition tool. Micro expression is one of the micro-languages of communication. Machine learning model developed in this study was Deep Learning with Convolutional Neural Network (CNN). The library that was used was Keras, this was used to recognize micro expression pattern. Additionally, OpenCV was also used for the general face recognition process. Both libraries were operated using Python programming language. The result of the micro expression test involving thirty participants detected three types of facial expression, namely joy, sadness, and anger expression. However, face recognition applied in the present study still needed some improvements, especially for anger and sadness expression. With regard to joy expression, 89% of the expression were recognized. Based on the recorded data, it is necessary to improve the recordings criteria to obtain a clearer expression.