Abstract Activity recognition is a very active area of research in computer vision. During the last decade, several efforts have focused on recognizing human activities in the wild from unedited videos posted in Youtube or captured by video-surveillance cameras. More recently, following the success of wearable cameras, a lot of interest has shifted towards the analysis of activities captured from a first-person point of view. In this chapter, we focus on the problem of activity recognition from visual lifelogs, that is, a set of images captured at regular intervals from a wearable photo-camera, as opposite to egocentric video shots. After describing the problem and the challenges it involves, we provide a detailed picture of the state of the art, also supported by an experimental survey of existing methods. Finally, we highlight limitations of the current approaches for real world applications and, in view of our analysis, we outline future challenges and research lines.