抄録
Facial image identification has become important in forensic science because surveillance cameras are popularly used as silent witnesses at potential crime scenes. In general, three methods are used for facial image identification: morphological comparison of facial features, facial image anthropometry and face-to-face superimposition. The most commonly employed method in actual casework is morphological comparison based on the morphological classification of facial components such as facial types, eyebrows, eyes, nose, lips and ears. However, classification for ear morphology has not been developed, except for the ear lobe. The human ear has various anatomical parts, including the helix, antihelix, tragus, antitragus, scaphoid fossa, and ear lobe. The present study was designed to develop a new classification system for the ear.
Ear images obtained from 164 Japanese adult males (n=94, age range: 24-60) and females (n=70, age range: 20-54) were used for establishing the morphological classification. In general, the features of the ear are very easily influenced by camera angle, which usually make it difficult to compare facial images taken with surveillance cameras to mug shots in actual casework. In our previous study, however, it was suggested that some components of the ear are little affected by camera angles if the scaphoid fossa could be found on the image even though the external acoustic meatus could not be found. Therefore, the morphological characteristics of some components such as the antihelix, tragus, antitragus, scaphoid fossa, and ear lobe were observed in the ear images from all persons, and morphological classification items for those components were established. All data classified according to these items were analyzed to obtain their frequency distribution in each component and to clarify the correlation between morphological characteristics. The results revealed that some morphological characteristics in each component were significantly correlated.
The present classification system for ear morphology would be useful for facial image identification.
