Indoor video surveillance systems often use the face modality to establish the identity of a person of interest. However, the face image may not offer sufficient discriminatory information in many scenarios due to substantial variations in pose, illumination, expression, resolution and distance between the subject and the camera. In such cases, the inclusion of an additional biometric modality can benefit the recognition process. In this regard, we consider the fusion of voice and face modalities for enhancing the recognition accuracy. The main contribution of this work is assembling a multimodal (face and voice), semi-constrained, indoor video surveillance dataset referred to as the MSU Audio-Video Indoor Surveillance (MSU-AVIS) dataset. We use a consumer-grade camera with a built-in microphone to acquire data for this purpose. We use current state-of-art deep-learning based methods to perform face and speaker recognition on the collected dataset for establishing baseline performance. We also explore multiple fusion schemes to combine face and speaker recognition to perform effective person recognition on audio-video surveillance data. Experiments convey the efficacy of the proposed multimodal fusion scheme (face and voice) over unimodal approaches in surveillance scenarios. The collected dataset is being made available for research purposes.