The latest articles on Forem by F.SAHFEERUL WASIHF (@wasihf). https://forem.com/wasihf https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3373736%2F7de8c635-de85-48dd-b763-22ce636f9a51.jpg https://forem.com/wasihf en F.SAHFEERUL WASIHF Mon, 21 Jul 2025 05:20:02 +0000 https://forem.com/wasihf/building-my-first-mobile-app-using-react-native-1e6 https://forem.com/wasihf/building-my-first-mobile-app-using-react-native-1e6 <h2> 🚀 Introduction: </h2> <p>During my internship at ACIC-KIF, I built two mobile apps—one of them was a smart NFC Tag Reader. This blog walks through the process of building my first mobile app using React Native.</p> <h2> 🛠️ Tech Stack: </h2> <p>React Native</p> <p>JavaScript</p> <p>Expo</p> <p>React Native NFC Manager</p> <p>Firebase</p> <h2> 📱 App Features: </h2> <p>Detect and read NFC tags</p> <p>Display content from NFC in real time</p> <p>Responsive mobile UI</p> <p>Real-device testing</p> <h2> 🚧 Challenges I Faced: </h2> <p>NFC support is not available in all Android phones</p> <p>Handling permission requests was tricky</p> <p>Debugging device-specific behavior</p> <h2> 🌟 What I Gained: </h2> <p>Full app lifecycle understanding</p> <p>How to handle hardware interactions</p> <p>Firebase integration basics</p> mobile reactnative internship nfc F.SAHFEERUL WASIHF Mon, 21 Jul 2025 05:06:46 +0000 https://forem.com/wasihf/how-i-built-an-ai-powered-face-recognition-app-from-scratch-1k18 https://forem.com/wasihf/how-i-built-an-ai-powered-face-recognition-app-from-scratch-1k18 <h2> 🚀 Introduction: </h2> <p>Inspired by how streaming platforms measure actor screen time, I built a face recognition system from scratch. This project detects faces in a movie, groups them into characters, and calculates how long each face appears on screen.</p> <h2> 🛠️ Tech Stack: </h2> <p>Python (OpenCV, Matplotlib)</p> <p>RetinaFace for face detection</p> <p>FaceNet for face recognition</p> <p>DBSCAN for clustering</p> <p>Jupyter Notebook for development</p> <h2> 📚 Approach: </h2> <p>Read video frame-by-frame using OpenCV</p> <p>Detect faces using RetinaFace</p> <p>Generate embeddings using FaceNet</p> <p>Cluster similar faces</p> <p>Ask user to label clusters manually</p> <p>Compute screen time per face</p> <h2> 📈 What I Learned: </h2> <p>Practical pipeline development in computer vision</p> <p>Face detection vs. recognition trade-offs</p> <p>Using unsupervised learning to cluster images</p> <h2> 🎁 Results: </h2> <p>Final output shows face clusters and their screen time in visual plots.</p> <p>Face images saved and labeled.</p> <p>Project ready for real-world analysis use cases.</p> <p><strong>🔗 Check it out:</strong> <a href="https://github.com/SAHFEERULWASIHF/ScreenTimeCalculator" rel="noopener noreferrer">GitHub Repo</a></p> python computervision facerecognition machinelearning