Multimodal biometric can overcome the limitations of the single biometric trait and gives better classification accuracy. This paper proposes a face-iris multimodal biometric system based on fusion at the matching score level. The iris recognition system is composed of segmentation, normalization, feature encoding and matching. The wavelet transform is used in feature extraction to generate a compact feature vector length of 128 bits; this reduces the computational time and storage of the iris code. The face recognition system is composed of enhancement, feature encoding and matching. The new method called 'Phase-based Gabor Fisher Classifier (PBGFC)' is used in feature extraction; PBGFC employs only 16 Gabor filters, i.e., filters with 2 scales and eight orientations. This fact makes the resulting feature vector for the PBGFC method very compact. The scores from iris and face are then combined using several score normalization and fusion techniques. To validate our approach, experiments are conducted on the iris and face images obtained from the CASIA and ORL datasets respectively. The results show that our multimodal biometric system achieves higher accuracy than both single biometric approaches and the other existing multi-biometric systems based on fusion of iris and face.

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