In this talk, I will present the work on feature-level fusion method for multimodal biometric recognition. Traditional methods for combining outputs from different modalities are based on score-level or decision-level fusion. Feature-level fusion can be more discriminative, but has hardly been explored due to challenges of different feature outputs and high feature dimensions. Here, I will present a framework using joint sparsity to combine information, and show its application to multimodal biometric recognition, face recognition and vidoe-based recognition.