Unlike the traditional learning-based, template-based and sparse-ba sed approaches, this paper presents a novel feature extraction algorithm based on texture and feature selection for automatic target recognition (ATR) in infrared imagery.Firstly, combining with the characteristics of the forward-looking-infrared (FLIR) imag es,we introduce a new concav e-convex local ternary pattern (CCLTP) operator by incorporating global intensity information,which divides the local features LTP into two distinct groups,namely,convex LTP and concave LTP.After that,different feature selection methods are discussed and tested to reduce the dimensionality of the features.Finally,the reduced feature is used for forward-looking infrared target recognition.Experi mental results demonstrate that the proposed method can achieve competitive results (at lower computational complexity) compared with th e state-of-the-art methods.