Introduction to Pattern Recognition1

Informally, a pattern is defined by the common denominator among the multiple instances of an entity. For example, commonality in all fingerprint images defines the fingerprint pattern; the commonality in fingerprint images of John Doe’s left index finger defines the John-Doe-left-index-fingerprint pattern (see Figure 1 showing a bunch of fingerprints of the same finger; and a bunch of impressions of arbitrary fingers in Figure 2). Thus, a pattern could be a fingerprint image, a handwritten cursive word, a human face, a speech signal, a bar code, or a web page on the Internet (see Figure 3). Often, individual patterns may be grouped into a category based on their common properties; the resultant group is also a pattern and is often called a pattern class. Pattern recognition is the science for observing (sensing) the environment, learning to distinguish patterns of interest (e.g., animals) from their background (e.g., sky, trees, ground), and making sound decisions about the patterns (e.g., Fido) or pattern classes (e.g., a dog, a mammal, an animal).