<![CDATA[This paper describes the performance ofreading or recognizing an image using the fisherface algorithm. Fisherface algorithm is a combination of two previous methods, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) methods. Where Principal Component Analysis (PCA) will play a role in reducing the complexity of the interrelationships between a large number of observed variables into a relatively small number of linear combinations, which are referred to as principal components. LDA has almost the same function but with a different approach. Fisherface algorithm here plays a role to perform facial recognition or an image reading based on trained set that have been stored in a folder or directory of a database. This study aims to see how the performance of a fisherface in recognizing an image in several external conditions and how accurately the fisherface algorithm can distinguish several images features.Keywords: face recognition, biometrics, PCA, LDA, fisherface]]>
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