Several devices can capture images with a large number of people, including those of high resolution known as gigapixel images. Those images can be helpful for studies and investigations, such as finding people in a crowd. They can provide more details but it can become a hard and challenging work problem to identify someone in the crowd. In this paper, we aim to help the work of a human observer with larger images with crowds by reducing the search space for several images to a ranking of ten images related to a specific person. Our model collect faces in a crowded Gigapixel image and then searching for people through the use of three different poses (front, right and left). To evaluate our method, we built a handcraft dataset with 42 people and we achieved a recognition rate of 69% in total dataset. We highlight that from the 31% ``not found'' between first ten in the ranking, many of them are very close to this boundary and, also 92% of non-matched are occluded by some accessory or by another face. Results shown great potential of our method to help a human observer find people in crowd, especially cluttered images, by providing him with a reduced search space.