Internet of Things (IoT) is enabling our lives in new and interesting ways, but with that comes the challenge of analyzing and bringing meaning to the stream of continuously generated data. One IoT trend in the home is the use of multiple security cameras for monitoring purposes, resulting in large amounts of data generated from images and video. For example, one house with 12 cameras taking 180,000 images per day can easily generate 5 GB of data. These large amounts of data make manual analysis impractical. Some cameras have built-in motion sensors to only take images when change is detected, and while this helps to reduce the data, light changes and other insignificant movement will still be picked up and have to be sorted through. To monitor the home for what is wanted, OpenCV* presents a promising solution. For the purposes of this paper it is people and faces. OpenCV* already has a number of pre-defined algorithms to search images for faces, people, and objects, and can also be trained to recognize new ones.