Nowadays, cloud architecture is getting more and more popular, so biometrics with cloud computing is becoming a trend for many applications. As a relative new biometrics, palm vein recognition has many merits, such as user friendly, high accuracy and robust. It is very convenient to deploy palm vein recognition in cloud computing, for example, using a cell phone to capture a palm vein image and fulfilling comparison in cloud environment. Usually, to reduce computation burden in a cell phone and data transmission, a palm vein image is compressed before transmission. However, how image compression affect recognition accuracy is not well studied. This paper empirically studies JPG compression for three kinds of palm vein feature extraction methods. It is found that subspace method is robust, texture-based method is sensitive, while line-based method is moderate, to image compression.