Label　imbalance　and　the　insufficiency　of　labeled　training　samples　are　major　obstacles　in　most　methods　for　counting　people　in　images　or　videos.　In　this　work,　a　sparse　representation-based　semi-supervised　regression　method　is　proposed　to　count　people　in　images　with　limited　data.　The　basic　idea　is　to　predict　the　unlabeled　training　data,　select　reliable　samples　to　expand　the　labeled　training　set,　and　retrain　the　regression　model.　In　the　algorithm,　the　initial　regression　model,　which　is　learned　from　the　labeled　training　data,　is　used　to　predict　the　number　of　people　in　the　unlabeled　training　dataset.　Then,　the　unlabeled　training　samples　are　regarded　as　an　over-complete　dictionary.　Each　feature　of　the　labeled　training　data　can　be　expressed　as　a　sparse　linear　approximation　of　the　unlabeled　data.　In　turn,　the　labels　of　the　labeled　training　data　can　be　estimated　based　on　a　sparse　reconstruction　in　feature　space.　The　label　confidence　in　labeling　an　unlabeled　sample　is　estimated　by　calculating　the　reconstruction　error.　The　training　set　is　updated　by　selecting　unlabeled　samples　with　minimal　reconstruction　errors,　and　the　regression　model　is　retrained　on　the　new　training　set.　A　co-training　style　method　is　applied　during　the　training　process.　The　experimental　results　demonstrate　that　the　proposed　method　has　a　low　mean　square　error　and　mean　absolute　error　compared　with　those　of　state-of-the-art　people-counting　benchmarks.