The shape analysis of otoliths, which are calcified structures in the inner ear of teleostean fishes, is known to be particularly relevant to address species identification and stock discrimination. Generally, scientists use classical methodologies of statistical analysis and shape recognition such as Fourier shape descriptors and Principal Component Analysis (PCA). These methods are subject to several limitations mainly to their incapacity to locate irregularities because they are based on global characterization of shape. Recently, more advanced techniques are proposed in this context in order to improve classification accuracies. The first recent method exploits the potential of shape geodesics which rely on local shape features for classification issues. The second one addresses the Best-Basis paradigm which combines the Wavelet Transform, and the potential of statistical analysis in order to fully automate the selection process of efficient features for classification. These methods have been shown to significantly outperform the standard approaches but they are not compared together yet. This study compare these two methods on a real dataset. The comparison is performed on 600 striped red mullet calcified structures collected for the NESPMAN European project. For each method, performances are reported for the classification of samples coming from three geographical zones in the Northwest European seas: the Bay of Biscay, a mixing zone composed of the Celtic Sea and the Western English Channel and a northern zone composed of the Eastern English Channel and the North Sea. Comparison shows that both methods lead to same conclusions.