Human Authentication using Face, Voice and Fingerprint Biometrics

Multimodal biometric approaches are growing in importance for personal verification and identification, since they provide better recognition results and hence improve security compared to biometrics based on a single modality. In this project, we present a multimodal biometric system that is based on the fusion of face, voice and fingerprint biometrics. For face recognition, we employ Haar Cascade Algorithm, while minutiae extraction is used for fingerprint recognition and we will be having a stored code word for the voice authentication, if any of these two authentication becomes true, the system consider the person as authorized person. Fusion at matching score level is then applied to enhance recognition performance. In particular, we employ the product rule in our investigation. The final identification is then performed using a nearest neighbour classifier which is fast and effective. Experimental results confirm that our approach achieves excellent recognition performance, and that the fusion approach outperforms biometric identification based on single modalities.

End-to-End Automated Latent Fingerprint Identification With Improved DCNN-FFT Enhancement

Automatic Latent Fingerprint Identification Systems (AFIS) are most widely used by forensic experts in law enforcement and criminal investigations. One of the critical steps used in automatic latent fingerprint matching is to automatically extract reliable minutiae from fingerprint images. Hence, minutiae extraction is considered to be a very important step in AFIS. The performance of such systems relies heavily on the quality of the input fingerprint images. Most of the state-of-the-art AFIS failed to produce good matching results due to poor ridge patterns and the presence of background noise. To ensure the robustness of fingerprint matching against low quality latent fingerprint images, it is essential to include a good fingerprint enhancement algorithm before minutiae extraction and matching. In this paper, we have proposed an end-to-end fingerprint matching system to automatically enhance, extract minutiae, and produce matching results. To achieve this, we have proposed a method to automatically enhance the poor-quality fingerprint images using the “Automated Deep Convolutional Neural Network (DCNN)” and “Fast Fourier Transform (FFT)” filters. The Deep Convolutional Neural Network (DCNN) produces a frequency enhanced map from fingerprint domain knowledge. We propose an “FFT Enhancement” algorithm to enhance and extract the ridges from the frequency enhanced map. Minutiae from the enhanced ridges are automatically extracted using a proposed “Automated Latent Minutiae Extractor (ALME)”. Based on the extracted minutiae, the fingerprints are automatically aligned, and a matching score is calculated using a proposed “Frequency Enhanced Minutiae Matcher (FEMM)” algorithm. Experiments are conducted on FVC2002, FVC2004, and NIST SD27 latent fingerprint databases. The minutiae extraction results show significant improvement in precision, recall, and F1 scores. We obtained the highest Rank-1 identification rate of 100% for FVC2002/2004 and 84.5% for NIST SD27 fingerprint databases. The matching results reveal that the proposed system outperforms state-of-the-art systems.

PENINGKATAN KUALITAS CITRA SIDIK JARI DENGAN ALGORITMA MINUTIAE EXTRACTION DAN LEARNING VECTOR QUANTIZATION

Sistem pengenalan sidik jari bertujuan untuk mengindentifikasi seseorang. Kendala utama dalam pengenalan sidik jari seseorang, adalah karena citranya memiliki kualitas yang rendah. Kualitas tersebut disebabkan oleh jenis kulit yang berminyak, kering, kotor dan jenis scanner fingerprint yang digunakan. Untuk itu dilakukan peningkatan dengan tujuan memperbaiki citra sidik jari sebagai faktor utama penentu tingkat akurasi hasil pengenalan citra sidik jari. Agar citra sidik jari yang tidak jelas mudah diinterpretasi oleh manusia maupun mesin, maka perlu peningkatan kualitas citra tersebut dengan cara memperjelas garis sidik jarinya. Penelitian ini bertujuan untuk peningkatan kualitas citra sidik jari dengan Algoritma Minutiae Extraction dan metode Learning Vector Quantization (LVQ) sebagai pengujian. Pengujian ini dilakukan dengan menggunakan 4 citra sebagai pelatihan pada setiap kelasnya. Adapun jumlah kelas pada pengujian ini seluruhnya 10 kelas dengan tiap kelas terdiri dari 10 citra. Pada pengujian 75 data citra sidik jari diperoleh tingkat akurasi 83.3333%.