ABDULHAMEED IDRIS ADEDAMOLA

I-GOA Feature Selection for Multimodal Biometrics Why I Added Feature Selection Using I-GOA in My Biometric System Introduction This article discusses the motivation behind integrating feature selection using the Improved Golf Optimization Algorithm (I-GOA) in a multimodal biometric recognition system based on fingerprint and palm vein traits. Disclaimer: This article presents general concepts and methodology discussions from an ongoing MPhil research project titled “Development of an Improved Golf-Optimization-Based Feature Selection Technique for Palm Vein and Fingerprint Recognition System.” The content is shared strictly for academic, educational, and research discussion purposes only. Detailed implementation, unpublished results, and proprietary research findings are not disclosed. Questions and Answers Why did you include a feature selection stage when some biometric system guides do not show it? Many biometric system diagrams provide simplified...

🚨 How to Prevent Leaking Your Android Keystore (.jks) on GitHub One common but dangerous mistake Android developers make is accidentally committing their keystore (.jks) file to Git. This can expose your app signing key and compromise your app on the Play Store. 🔍 The Problem Even if you delete a .jks file from your project, Git may still be tracking it. That means it can still be pushed to GitHub — which is a serious security risk. ✅ Step 1: Add .jks to .gitignore Open your .gitignore file (in the root of your project) and add: *.jks This tells Git to ignore all keystore files in your project. ⚠️ Step 2: Remove Already Tracked Files Important: .gitignore does NOT remove files that Git is already tracking. You must remove them manually. git rm --cached upload-key.jks git rm --cached app/upload-key.jks This removes the files from Git tracking but keeps them on your local machine. 🔎 Step 3: Verify It Works Check if Git is still tracking any .jk...

CNN-Based Biometric Recognition Blog CNN-Based Biometric Recognition Using Palmprint and Fingerprint Images Biometric recognition systems are widely used in modern security because they provide reliable identity verification based on unique human traits such as fingerprints and palmprints. However, unimodal systems often suffer from limitations such as noise sensitivity, variability in data, and reduced performance under poor image quality. Approach This work presents a Convolutional Neural Network (CNN) approach using two biometric modalities: palmprint and fingerprint images. The objective is to develop and evaluate independent CNN models for each modality as a foundation for future multimodal biometric fusion. Datasets and Preprocessing The BMPD dataset was used for palmprint recognition, while the FVC2004 DB1-B dataset was used f...