The LBP (Local Binary Pattern) texture descriptor has demonstrated its superiority in several image applications texture characterization, facial identification and macro-expression recognition. Featuring by LBP characterizes image by its local structures, observing its micro patterns and building a histogram. Each observed pixel is featured and then encoded into one byte. All these codes constitute bins for the histogram. For an efficient classification, encoded bytes can be divided into uniform and non-uniform codes. In standard applications, only uniform codes are used leading to 59 codes. The present work proposes an additional process for the reduction of these codes. The proposal is developed and comparatively evaluated with success to classical LBP. Experimental evaluations are performed on 2 different databases for facial identification and macro-expression recognition respectively, and this for different reduction of code length. Though for macro-expression recognition the proposed features can give lower but comparable performance with the traditional LBP, for facial identification they perform very well and keep excellent efficiency. This approach can be extended to most part of LBP variants while keeping the simplicity of LBP. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.