Micro-expression recognition (MER) aims to uncover genuine emotions and underlying psychological states. However, existing MER methods struggle with three main challenges. 1) Scarcity of micro-expression samples. 2) Difficulty in modeling nearly imperceptible facial movements. 3) Reliance on apex frame annotations. To address these issues, we propose a Self-supervised Oriented Deformation model for Apex-free Micro-expression Recognition (SODA4MER). Our approach enhances local deformation perception using muscle-group priors and amplifies subtle features through Dynamic Stereotype Theory (DST) based enhancement, while contrastive learning eliminates the need for manual apex annotations. Specifically, the Oriented deformation estimator of SODA4MER is first pre-trained in a self-supervised manner. Secondly, a Gated Temporal Variance Gaussian model (GTVG) is introduced to adaptively integrate facial muscle-group priors, enhancing local deformation perception and mitigating noise from head movements. Then, contrastive learning is employed to achieve apex detection by identifying the frame with the most significant local deformation. Finally, guided by DST, we introduced a feature enhancement strategy that models the temporal dynamics of local deformation in the activation and decay phases, leading to richer deformation features. Our rigorous experiments confirm the competitive performance and practical applicability of SODA4MER.