Journals Proceedings

Abstract

In this research, we develop a facial image retrieval computational model for the problem of facial images retrieval by integrating content-based image retrieval (CBIR) techniques and face recognition techniques(FERET) , with the verbal description of the semantic features of the facial image . Eigenfaces is applied to extract the characteristic feature images of the human face images. One hundred participant participated to choose , order and annotate the semantic features of the human face based on the importance of each features to differentiate between human faces . During the retrieval process system use the specific semantic features , of the face that is user looking for , to narrow down the search space . Eigenfaces is then projected onto the narrowed down human faces search space to identify and retrieve the similar faces to the query face from the database .Euclidean distance is used for classification purpose . The database that is used consists of 1500 local facial images database of one hundred and fifty participants from the University of Malaya (UM), Kuala Lumpur,and some of their friends and families outside the UM.The proposed human facial image retrieval is evaluated through several experiments. Precision and Recall are used for results evaluation .The results are encouraging comparing to typical facial image retrieval techniques.